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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import xarray as xr\n",
"import numpy as np\n",
"import itertools\n",
"\n",
"from rooftop_handling import link_rooftops_with_pixels\n",
"import util\n",
"\n",
"from sklearn import tree\n",
"from sklearn.ensemble import RandomForestRegressor\n",
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.neighbors import KNeighborsRegressor\n",
"from sklearn.neural_network import MLPRegressor\n",
"from sklearn.svm import SVR\n",
"from sklearn.metrics import mean_squared_error as mse\n",
"from sklearn.metrics import mean_absolute_error as mae\n",
"from sklearn.metrics import r2_score as r2\n",
"from sklearn.model_selection import cross_val_predict\n",
"from sklearn.preprocessing import Normalizer\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.base import clone\n",
"from ELM_ensemble import ELM_Ensemble\n",
"from scipy.stats import describe\n",
"from uncertainty import Uncertainty\n",
"\n",
"import os\n",
"import time\n",
"\n",
"%matplotlib notebook\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib.colors import LinearSegmentedColormap"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"colorsRGB = [(47,111,191),\n",
" (51,148,190),\n",
" (65,195,153),\n",
" (105,219,77),\n",
" (183,233,81),\n",
" (249,239,88),\n",
" (250,201,53),\n",
" (246,135,26),\n",
" (220,82,7),\n",
" (193,31,4)]\n",
"colors = []\n",
"for row in colorsRGB:\n",
" colors.append(tuple([i/255.0 for i in row]))\n",
" \n",
"cm_PV = LinearSegmentedColormap.from_list('radiation', colors, N=500)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def rescale(vector, training):\n",
" return (vector * training.std()) + training.mean()\n",
"\n",
"def normalize(vector, training):\n",
" return (vector - training.mean()) / training.std()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def notnan( x ):\n",
" return x[~np.isnan(x)]\n",
"\n",
"# ERROR MEASUREMENTS\n",
"error = {\n",
" 'MSE' : lambda y, t: mse(t,y),\n",
" 'RMSE': lambda y, t: np.sqrt(mse(t,y)),\n",
" 'MAE' : lambda y, t: mae(t,y),\n",
" 'MAEP': lambda y, t: mae(np.ones(len(y)), notnan(y/t)),\n",
" 'MBE' : lambda y, t: np.mean(t - y),\n",
" 'MBEP': lambda y, t: np.mean(1 - notnan(y/t)),\n",
" 'R2' : lambda y, t: r2(t,y)\n",
"}\n",
"\n",
"def get_errors(true, pred, label = ''):\n",
" # create a pandas series with errors and print them\n",
" true = true.reshape((-1,1))\n",
" pred = pred.reshape((-1,1))\n",
" \n",
" err_series = pd.Series(data = np.zeros(len(error)), index = error.keys())\n",
" for key, val in error.items():\n",
" print('%s %s: %f' %(label, key, val(pred, true)))\n",
" err_series[ key ] = val(pred, true)\n",
" \n",
" return err_series"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def shp( vector ):\n",
"# check shape of vector and reshape it to one dimension if it is a one-dimensional vector\n",
" if len( label_name ) == 1:\n",
" return vector.reshape((-1,1))\n",
" else:\n",
" return vector "
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def compute_residuals( prediction, target, model_std):\n",
" residuals = ( prediction - target )**2 - model_std**2\n",
" return np.sqrt( np.maximum(residuals, 0) ) "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"path = '/Volumes/Seagate Backup Plus Drive/DOKUMENTE/EPFL/data/Results/PV_w_uncertainty/pv_prediction/'\n",
"# path = '/work/hyenergy/output/solar_potential/technical_potential/predict_pv'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Load data"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Applications/anaconda2/envs/py3/lib/python3.6/site-packages/numpy/lib/arraysetops.py:472: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
" mask |= (ar1 == a)\n"
]
}
],
"source": [
"features = pd.read_csv(os.path.join(path, 'features_v2.csv'), index_col = 0)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Applications/anaconda2/envs/py3/lib/python3.6/site-packages/numpy/lib/arraysetops.py:472: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
" mask |= (ar1 == a)\n"
]
}
],
"source": [
"targets = pd.read_csv(os.path.join(path, 'targets_v2.csv'), index_col = 0)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Applications/anaconda2/envs/py3/lib/python3.6/site-packages/numpy/lib/arraysetops.py:472: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
" mask |= (ar1 == a)\n"
]
}
],
"source": [
"communes = pd.read_csv(os.path.join(path, 'data_all.csv'), index_col = 0,\n",
" usecols = ['DF_UID', 'Kanton', 'Commune_ID', 'XCOORD', 'YCOORD', 'FLAECHE', 'GWR_EGID'])"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"data = pd.concat([features, targets, communes], axis = 1).dropna()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>NEIGUNG</th>\n",
" <th>AUSRICHTUNG</th>\n",
" <th>SIS</th>\n",
" <th>SISDIR</th>\n",
" <th>DHI</th>\n",
" <th>SVF</th>\n",
" <th>illumination</th>\n",
" <th>horizon_S</th>\n",
" <th>horizon_E</th>\n",
" <th>horizon_NEE</th>\n",
" <th>...</th>\n",
" <th>horizon_SSE</th>\n",
" <th>horizon_SEE</th>\n",
" <th>MSTRAHLUNG</th>\n",
" <th>PV_kWh_m2</th>\n",
" <th>FLAECHE</th>\n",
" <th>XCOORD</th>\n",
" <th>YCOORD</th>\n",
" <th>GWR_EGID</th>\n",
" <th>Kanton</th>\n",
" <th>Commune_ID</th>\n",
" </tr>\n",
" <tr>\n",
" <th>DF_UID</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
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" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>5343176</th>\n",
" <td>16</td>\n",
" <td>-17.0</td>\n",
" <td>1350.071526</td>\n",
" <td>846.491412</td>\n",
" <td>503.587476</td>\n",
" <td>0.718741</td>\n",
" <td>0.811781</td>\n",
" <td>6.092388</td>\n",
" <td>14.689941</td>\n",
" <td>17.452604</td>\n",
" <td>...</td>\n",
" <td>8.197643</td>\n",
" <td>9.963911</td>\n",
" <td>1548</td>\n",
" <td>210.528</td>\n",
" <td>108.129956</td>\n",
" <td>722110.3750</td>\n",
" <td>75509.609375</td>\n",
" <td>11157515.0</td>\n",
" <td>TI</td>\n",
" <td>5250.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5343175</th>\n",
" <td>17</td>\n",
" <td>163.0</td>\n",
" <td>1350.071526</td>\n",
" <td>846.491412</td>\n",
" <td>503.587476</td>\n",
" <td>0.759114</td>\n",
" <td>0.804061</td>\n",
" <td>11.788798</td>\n",
" <td>16.118021</td>\n",
" <td>15.847362</td>\n",
" <td>...</td>\n",
" <td>13.261172</td>\n",
" <td>13.841580</td>\n",
" <td>1114</td>\n",
" <td>151.504</td>\n",
" <td>110.595071</td>\n",
" <td>722108.1875</td>\n",
" <td>75516.570312</td>\n",
" <td>11157515.0</td>\n",
" <td>TI</td>\n",
" <td>5250.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5343177</th>\n",
" <td>25</td>\n",
" <td>-59.0</td>\n",
" <td>1350.071526</td>\n",
" <td>846.491412</td>\n",
" <td>503.587476</td>\n",
" <td>0.706426</td>\n",
" <td>0.738014</td>\n",
" <td>12.730241</td>\n",
" <td>21.675634</td>\n",
" <td>21.338327</td>\n",
" <td>...</td>\n",
" <td>13.979061</td>\n",
" <td>15.757084</td>\n",
" <td>545</td>\n",
" <td>74.120</td>\n",
" <td>20.512037</td>\n",
" <td>722494.3125</td>\n",
" <td>75592.125000</td>\n",
" <td>11103162.0</td>\n",
" <td>TI</td>\n",
" <td>5250.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5343178</th>\n",
" <td>24</td>\n",
" <td>121.0</td>\n",
" <td>1350.071526</td>\n",
" <td>846.491412</td>\n",
" <td>503.587476</td>\n",
" <td>0.643597</td>\n",
" <td>0.654795</td>\n",
" <td>16.819239</td>\n",
" <td>18.360150</td>\n",
" <td>19.976019</td>\n",
" <td>...</td>\n",
" <td>12.246127</td>\n",
" <td>15.541741</td>\n",
" <td>598</td>\n",
" <td>81.328</td>\n",
" <td>20.850745</td>\n",
" <td>722491.9375</td>\n",
" <td>75593.523438</td>\n",
" <td>11103162.0</td>\n",
" <td>TI</td>\n",
" <td>5250.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5343214</th>\n",
" <td>11</td>\n",
" <td>168.0</td>\n",
" <td>1350.071526</td>\n",
" <td>846.491412</td>\n",
" <td>503.587476</td>\n",
" <td>0.598916</td>\n",
" <td>0.448989</td>\n",
" <td>43.407631</td>\n",
" <td>15.740415</td>\n",
" <td>5.747942</td>\n",
" <td>...</td>\n",
" <td>44.075201</td>\n",
" <td>35.429473</td>\n",
" <td>1032</td>\n",
" <td>140.352</td>\n",
" <td>168.164462</td>\n",
" <td>722125.8125</td>\n",
" <td>75788.093750</td>\n",
" <td>11157513.0</td>\n",
" <td>TI</td>\n",
" <td>5250.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 24 columns</p>\n",
"</div>"
],
"text/plain": [
" NEIGUNG AUSRICHTUNG SIS SISDIR DHI SVF \\\n",
"DF_UID \n",
"5343176 16 -17.0 1350.071526 846.491412 503.587476 0.718741 \n",
"5343175 17 163.0 1350.071526 846.491412 503.587476 0.759114 \n",
"5343177 25 -59.0 1350.071526 846.491412 503.587476 0.706426 \n",
"5343178 24 121.0 1350.071526 846.491412 503.587476 0.643597 \n",
"5343214 11 168.0 1350.071526 846.491412 503.587476 0.598916 \n",
"\n",
" illumination horizon_S horizon_E horizon_NEE ... horizon_SSE \\\n",
"DF_UID ... \n",
"5343176 0.811781 6.092388 14.689941 17.452604 ... 8.197643 \n",
"5343175 0.804061 11.788798 16.118021 15.847362 ... 13.261172 \n",
"5343177 0.738014 12.730241 21.675634 21.338327 ... 13.979061 \n",
"5343178 0.654795 16.819239 18.360150 19.976019 ... 12.246127 \n",
"5343214 0.448989 43.407631 15.740415 5.747942 ... 44.075201 \n",
"\n",
" horizon_SEE MSTRAHLUNG PV_kWh_m2 FLAECHE XCOORD \\\n",
"DF_UID \n",
"5343176 9.963911 1548 210.528 108.129956 722110.3750 \n",
"5343175 13.841580 1114 151.504 110.595071 722108.1875 \n",
"5343177 15.757084 545 74.120 20.512037 722494.3125 \n",
"5343178 15.541741 598 81.328 20.850745 722491.9375 \n",
"5343214 35.429473 1032 140.352 168.164462 722125.8125 \n",
"\n",
" YCOORD GWR_EGID Kanton Commune_ID \n",
"DF_UID \n",
"5343176 75509.609375 11157515.0 TI 5250.0 \n",
"5343175 75516.570312 11157515.0 TI 5250.0 \n",
"5343177 75592.125000 11103162.0 TI 5250.0 \n",
"5343178 75593.523438 11103162.0 TI 5250.0 \n",
"5343214 75788.093750 11157513.0 TI 5250.0 \n",
"\n",
"[5 rows x 24 columns]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"workdir = '/Users/alinawalch/Documents/EPFL/data/interactive_visualisation'\n",
"# workdir = '/work/hyenergy/raw/Surface_data/swissBOUNDARIES3D/BOUNDARIES_2019/DATEN/swissBOUNDARIES3D/SHAPEFILE_LV03_LN02'\n",
"commune_data = pd.read_csv(os.path.join(workdir, 'swissBOUNDARIES3D_1_3_TLM_HOHEITSGEBIET_data.csv'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Part 1: Define features & normalize"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"label_name = ['MSTRAHLUNG']\n",
"identifier = 'DF_UID'\n",
"\n",
"all_features = features.columns\n",
"# feature_labels = ['Roof tilt', 'Roof aspect', 'Annual GHI', 'Horizon (S)', 'Horizon (E)', 'Horizon (W)']\n",
"\n",
"feature_names = ['SIS', 'horizon_SEE', 'horizon_SWW', 'horizon_S', 'NEIGUNG', 'AUSRICHTUNG']\n",
"\n",
"feature_labels = ['Annual GHI', 'Horizon SEE (120°)', 'Horizon SWW (240°)', 'Horizon S (180°)', \n",
" 'Roof tilt', 'Roof aspect', ]"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"def get_data( data, feature_names, label_name ):\n",
" X = data.loc[ : , feature_names ].values\n",
" T = data.loc[ : , label_name ].values.reshape((-1,))\n",
"\n",
" norm = Normalizer()\n",
" x = norm.fit_transform(X)\n",
" t = normalize(T, T)\n",
" \n",
" return x, t, X, T"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"def get_mse( x, t, model, cv = 5, print_time = True ):\n",
" \n",
" est = set_estimators()[ model ]\n",
"\n",
" # normalized cross-validation\n",
" tt = time.time()\n",
" y = cross_val_predict(est, x, t, n_jobs = -1) # cannot use n_jobs = -1 as not all are scikit-learn estimators\n",
" cv_time = time.time()-tt\n",
" if print_time : \n",
" print('Crossvalidation time: %.3fs' %cv_time)\n",
"\n",
" # Y = rescale(y, T)\n",
" return mse(t, y), 0, cv_time # mse(t, y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Part 2: Get train and test areas"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Part 2.1: Choose training areas and testing areas (based on Cantons)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#'/work/hyenergy/output/solar_potential/technical_potential/interactive_visualisation'\n",
"workdir = '/Users/alinawalch/Documents/EPFL/data/interactive_visualisation'\n",
"communes = gpd.read_file(os.path.join(workdir, 'swissBOUNDARIES3D_1_3_TLM_HOHEITSGEBIET.shp'))"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"COMMUNE_FP = '/Users/alinawalch/Documents/EPFL/data/GWR/GDEListe_20150101.txt'\n",
"# COMMUNE_FP = '/work/hyenergy/raw/GWR_Reg-BL/GDEListe_20150101.txt'\n",
"commune_info = pd.read_csv( COMMUNE_FP, sep = '\\t', encoding = 'cp1250' )"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>GDEKT</th>\n",
" <th>GDEBZNR</th>\n",
" <th>GDENR</th>\n",
" <th>GDENAME</th>\n",
" <th>GDENAMK</th>\n",
" <th>GDEBZNA</th>\n",
" <th>GDEKTNA</th>\n",
" <th>GDEMUTDAT</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2058</th>\n",
" <td>VS</td>\n",
" <td>2302.0</td>\n",
" <td>6021.0</td>\n",
" <td>Ardon</td>\n",
" <td>Ardon</td>\n",
" <td>District de Conthey</td>\n",
" <td>Valais / Wallis</td>\n",
" <td>01.01.1960</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2059</th>\n",
" <td>VS</td>\n",
" <td>2302.0</td>\n",
" <td>6022.0</td>\n",
" <td>Chamoson</td>\n",
" <td>Chamoson</td>\n",
" <td>District de Conthey</td>\n",
" <td>Valais / Wallis</td>\n",
" <td>01.01.1960</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2060</th>\n",
" <td>VS</td>\n",
" <td>2302.0</td>\n",
" <td>6023.0</td>\n",
" <td>Conthey</td>\n",
" <td>Conthey</td>\n",
" <td>District de Conthey</td>\n",
" <td>Valais / Wallis</td>\n",
" <td>01.01.1960</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2061</th>\n",
" <td>VS</td>\n",
" <td>2302.0</td>\n",
" <td>6024.0</td>\n",
" <td>Nendaz</td>\n",
" <td>Nendaz</td>\n",
" <td>District de Conthey</td>\n",
" <td>Valais / Wallis</td>\n",
" <td>01.01.1960</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2062</th>\n",
" <td>VS</td>\n",
" <td>2302.0</td>\n",
" <td>6025.0</td>\n",
" <td>Vétroz</td>\n",
" <td>Vétroz</td>\n",
" <td>District de Conthey</td>\n",
" <td>Valais / Wallis</td>\n",
" <td>01.01.1960</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" GDEKT GDEBZNR GDENR GDENAME GDENAMK GDEBZNA \\\n",
"2058 VS 2302.0 6021.0 Ardon Ardon District de Conthey \n",
"2059 VS 2302.0 6022.0 Chamoson Chamoson District de Conthey \n",
"2060 VS 2302.0 6023.0 Conthey Conthey District de Conthey \n",
"2061 VS 2302.0 6024.0 Nendaz Nendaz District de Conthey \n",
"2062 VS 2302.0 6025.0 Vétroz Vétroz District de Conthey \n",
"\n",
" GDEKTNA GDEMUTDAT \n",
"2058 Valais / Wallis 01.01.1960 \n",
"2059 Valais / Wallis 01.01.1960 \n",
"2060 Valais / Wallis 01.01.1960 \n",
"2061 Valais / Wallis 01.01.1960 \n",
"2062 Valais / Wallis 01.01.1960 "
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# find french-speaking communes in Valais based on the start of their district name ('District' and not 'Bezirk')\n",
"districts_valais = commune_info[ commune_info.GDEKT == 'VS' ]\n",
"french_communes = [bzg[0:8] == 'District' for bzg in districts_valais.GDEBZNA.values]\n",
"districts_valais[french_communes].head()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"french_cantons = ['NE', 'JU', 'VD', 'GE', 'FR'] # plus french communes in Valais; considering FR even though bilangue"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"training_data = data[(data.Kanton.isin( french_cantons )) \\\n",
" | (data.Commune_ID.isin( districts_valais[french_communes].GDENR.values )) ]\n",
"training_data = training_data.dropna()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
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" <td>8.125061</td>\n",
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" <td>VS</td>\n",
" <td>6032.0</td>\n",
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" <th>9449168</th>\n",
" <td>27</td>\n",
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" <td>1.431431</td>\n",
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" <td>32.135801</td>\n",
" <td>579235.2500</td>\n",
" <td>79689.906250</td>\n",
" <td>902671.0</td>\n",
" <td>VS</td>\n",
" <td>6032.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9449167</th>\n",
" <td>25</td>\n",
" <td>-173.0</td>\n",
" <td>1552.478067</td>\n",
" <td>900.636293</td>\n",
" <td>651.867345</td>\n",
" <td>0.764098</td>\n",
" <td>0.753425</td>\n",
" <td>30.548385</td>\n",
" <td>3.495298</td>\n",
" <td>0.715425</td>\n",
" <td>...</td>\n",
" <td>28.331859</td>\n",
" <td>15.277346</td>\n",
" <td>1086</td>\n",
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" <td>579235.6875</td>\n",
" <td>79693.085938</td>\n",
" <td>902671.0</td>\n",
" <td>VS</td>\n",
" <td>6032.0</td>\n",
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" <tr>\n",
" <th>9449170</th>\n",
" <td>34</td>\n",
" <td>22.0</td>\n",
" <td>1552.478067</td>\n",
" <td>900.636293</td>\n",
" <td>651.867345</td>\n",
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" <td>2.499965</td>\n",
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" <td>6.787898</td>\n",
" <td>1572</td>\n",
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" <td>162.651604</td>\n",
" <td>579209.9375</td>\n",
" <td>79712.953125</td>\n",
" <td>902671.0</td>\n",
" <td>VS</td>\n",
" <td>6032.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9449157</th>\n",
" <td>33</td>\n",
" <td>22.0</td>\n",
" <td>1552.478067</td>\n",
" <td>900.636293</td>\n",
" <td>651.867345</td>\n",
" <td>0.785625</td>\n",
" <td>0.873858</td>\n",
" <td>17.481223</td>\n",
" <td>8.517506</td>\n",
" <td>8.390612</td>\n",
" <td>...</td>\n",
" <td>20.984736</td>\n",
" <td>10.082763</td>\n",
" <td>1630</td>\n",
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" <td>79714.609375</td>\n",
" <td>902671.0</td>\n",
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" </tr>\n",
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"</table>\n",
"<p>5 rows × 24 columns</p>\n",
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],
"text/plain": [
" NEIGUNG AUSRICHTUNG SIS SISDIR DHI SVF \\\n",
"DF_UID \n",
"9449166 0 0.0 1552.478067 900.636293 651.867345 0.794826 \n",
"9449168 27 7.0 1552.478067 900.636293 651.867345 0.755589 \n",
"9449167 25 -173.0 1552.478067 900.636293 651.867345 0.764098 \n",
"9449170 34 22.0 1552.478067 900.636293 651.867345 0.803094 \n",
"9449157 33 22.0 1552.478067 900.636293 651.867345 0.785625 \n",
"\n",
" illumination horizon_S horizon_E horizon_NEE ... horizon_SSE \\\n",
"DF_UID ... \n",
"9449166 0.816486 19.170645 8.315230 8.125061 ... 27.173886 \n",
"9449168 0.728311 30.907117 4.143541 1.431431 ... 29.991368 \n",
"9449167 0.753425 30.548385 3.495298 0.715425 ... 28.331859 \n",
"9449170 0.886675 21.041524 2.523374 2.499965 ... 21.660933 \n",
"9449157 0.873858 17.481223 8.517506 8.390612 ... 20.984736 \n",
"\n",
" horizon_SEE MSTRAHLUNG PV_kWh_m2 FLAECHE XCOORD \\\n",
"DF_UID \n",
"9449166 19.419470 1418 192.848 113.600622 579172.9375 \n",
"9449168 17.834692 1577 214.472 32.135801 579235.2500 \n",
"9449167 15.277346 1086 147.696 33.816775 579235.6875 \n",
"9449170 6.787898 1572 213.792 162.651604 579209.9375 \n",
"9449157 10.082763 1630 221.680 175.680874 579190.1875 \n",
"\n",
" YCOORD GWR_EGID Kanton Commune_ID \n",
"DF_UID \n",
"9449166 79689.500000 902671.0 VS 6032.0 \n",
"9449168 79689.906250 902671.0 VS 6032.0 \n",
"9449167 79693.085938 902671.0 VS 6032.0 \n",
"9449170 79712.953125 902671.0 VS 6032.0 \n",
"9449157 79714.609375 902671.0 VS 6032.0 \n",
"\n",
"[5 rows x 24 columns]"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"training_data.head()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1909876"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(training_data)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"testing_data = data[ np.logical_not(data.index.isin( training_data.index.values )) ]\n",
"testing_data = testing_data.dropna()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"6672012"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(testing_data)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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" <th>5343175</th>\n",
" <td>17</td>\n",
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" <th>5343177</th>\n",
" <td>25</td>\n",
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" <td>503.587476</td>\n",
" <td>0.706426</td>\n",
" <td>0.738014</td>\n",
" <td>12.730241</td>\n",
" <td>21.675634</td>\n",
" <td>21.338327</td>\n",
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" <td>15.757084</td>\n",
" <td>545</td>\n",
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" <td>20.512037</td>\n",
" <td>722494.3125</td>\n",
" <td>75592.125000</td>\n",
" <td>11103162.0</td>\n",
" <td>TI</td>\n",
" <td>5250.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5343178</th>\n",
" <td>24</td>\n",
" <td>121.0</td>\n",
" <td>1350.071526</td>\n",
" <td>846.491412</td>\n",
" <td>503.587476</td>\n",
" <td>0.643597</td>\n",
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" <td>15.541741</td>\n",
" <td>598</td>\n",
" <td>81.328</td>\n",
" <td>20.850745</td>\n",
" <td>722491.9375</td>\n",
" <td>75593.523438</td>\n",
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" <td>TI</td>\n",
" <td>5250.0</td>\n",
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" <th>5343214</th>\n",
" <td>11</td>\n",
" <td>168.0</td>\n",
" <td>1350.071526</td>\n",
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" <td>503.587476</td>\n",
" <td>0.598916</td>\n",
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" <td>35.429473</td>\n",
" <td>1032</td>\n",
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],
"text/plain": [
" NEIGUNG AUSRICHTUNG SIS SISDIR DHI SVF \\\n",
"DF_UID \n",
"5343176 16 -17.0 1350.071526 846.491412 503.587476 0.718741 \n",
"5343175 17 163.0 1350.071526 846.491412 503.587476 0.759114 \n",
"5343177 25 -59.0 1350.071526 846.491412 503.587476 0.706426 \n",
"5343178 24 121.0 1350.071526 846.491412 503.587476 0.643597 \n",
"5343214 11 168.0 1350.071526 846.491412 503.587476 0.598916 \n",
"\n",
" illumination horizon_S horizon_E horizon_NEE ... horizon_SSE \\\n",
"DF_UID ... \n",
"5343176 0.811781 6.092388 14.689941 17.452604 ... 8.197643 \n",
"5343175 0.804061 11.788798 16.118021 15.847362 ... 13.261172 \n",
"5343177 0.738014 12.730241 21.675634 21.338327 ... 13.979061 \n",
"5343178 0.654795 16.819239 18.360150 19.976019 ... 12.246127 \n",
"5343214 0.448989 43.407631 15.740415 5.747942 ... 44.075201 \n",
"\n",
" horizon_SEE MSTRAHLUNG PV_kWh_m2 FLAECHE XCOORD \\\n",
"DF_UID \n",
"5343176 9.963911 1548 210.528 108.129956 722110.3750 \n",
"5343175 13.841580 1114 151.504 110.595071 722108.1875 \n",
"5343177 15.757084 545 74.120 20.512037 722494.3125 \n",
"5343178 15.541741 598 81.328 20.850745 722491.9375 \n",
"5343214 35.429473 1032 140.352 168.164462 722125.8125 \n",
"\n",
" YCOORD GWR_EGID Kanton Commune_ID \n",
"DF_UID \n",
"5343176 75509.609375 11157515.0 TI 5250.0 \n",
"5343175 75516.570312 11157515.0 TI 5250.0 \n",
"5343177 75592.125000 11103162.0 TI 5250.0 \n",
"5343178 75593.523438 11103162.0 TI 5250.0 \n",
"5343214 75788.093750 11157513.0 TI 5250.0 \n",
"\n",
"[5 rows x 24 columns]"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"testing_data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Get cities"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"cities = ['Luzern', 'Zürich', 'Basel', 'Lugano', 'Interlaken', 'Davos']"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"city_data = {}\n",
"for name in cities:\n",
" city_data[name] = testing_data[ testing_data.Commune_ID.isin( \n",
" commune_info[ commune_info.GDENAME == name].GDENR.values) ].dropna()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"city_coords = pd.DataFrame(data = [], columns = ['XCOORD', 'YCOORD'])\n",
"\n",
"for name, data in city_data.items():\n",
" city_coords = city_coords.append( pd.DataFrame(data = data[['XCOORD', 'YCOORD']].mean(), \n",
" index = ['XCOORD', 'YCOORD'], columns = [ name ]).T)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>XCOORD</th>\n",
" <th>YCOORD</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Luzern</th>\n",
" <td>665624.955697</td>\n",
" <td>211666.076867</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Zürich</th>\n",
" <td>682773.044850</td>\n",
" <td>248311.436652</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Basel</th>\n",
" <td>611299.379711</td>\n",
" <td>267349.846960</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Lugano</th>\n",
" <td>718420.536632</td>\n",
" <td>97348.639209</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Interlaken</th>\n",
" <td>632237.358008</td>\n",
" <td>170485.718042</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Davos</th>\n",
" <td>781587.456246</td>\n",
" <td>183569.663515</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" XCOORD YCOORD\n",
"Luzern 665624.955697 211666.076867\n",
"Zürich 682773.044850 248311.436652\n",
"Basel 611299.379711 267349.846960\n",
"Lugano 718420.536632 97348.639209\n",
"Interlaken 632237.358008 170485.718042\n",
"Davos 781587.456246 183569.663515"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"city_coords"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plot locations of train and test data with cities"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Luzern', 'Zürich', 'Basel', 'Lugano', 'Interlaken', 'Davos'], dtype='object')"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"city_coords.index"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" if (mpl.ratio != 1) {\n",
" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
" }\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" fig.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"trn = training_data.sample(100000)\n",
"tst = testing_data.sample(1000000)\n",
"\n",
"DIST = 5000\n",
"XMAX = tst.XCOORD.max()\n",
"XMIN = trn.XCOORD.min()\n",
"\n",
"YMAX = tst.YCOORD.max()\n",
"YMIN = tst.YCOORD.min()\n",
"\n",
"plt.figure()\n",
"plt.scatter(trn.XCOORD, trn.YCOORD, s = 0.5, c = 'C0', label = 'training data')\n",
"plt.scatter(tst.XCOORD, tst.YCOORD, s = 0.5, c = 'C1', label = 'test data')\n",
"plt.scatter(city_coords.XCOORD, city_coords.YCOORD, s = 50, c = 'g', label = '')\n",
"\n",
"for city in city_coords.index:\n",
" plt.text(city_coords.loc[city].XCOORD + DIST, city_coords.loc[city].YCOORD, city, \n",
" fontsize = 12)\n",
"\n",
"plt.axis('equal')\n",
"plt.axis('off')\n",
"\n",
"plt.xlim((XMIN, XMAX))\n",
"plt.ylim((YMIN, YMAX))\n",
"\n",
"plt.legend(markerscale=5)\n",
"plt.tight_layout()\n",
"# plt.savefig('train_test_w_cities.png', dpi = 300)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Part 3: Define ML models"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"# ML MODEL PARAMETERS\n",
"n_est = 500\n",
"n_samples_leaf = 3\n",
"RF_features = 3\n",
"ELM_nodes = 45\n",
"ELM_est = 25\n",
"MLP_layers = (100, 20,) # WORKS RESONABLY WELL\n",
"SVM_paramC = 5\n",
"SVM_gamma = 200\n",
"knn_neighbors = 17\n",
"\n",
"k_CV = 5\n",
"\n",
"# DEFINITION OF ESTIMATORS\n",
"def set_estimators():\n",
" estimator = {\n",
" 'LIN': LinearRegression( n_jobs = -1 ),\n",
" 'KNN': KNeighborsRegressor( n_neighbors = knn_neighbors, n_jobs = -1 ),\n",
" 'ELM' : ELM_Ensemble( n_estimators = ELM_est, n_nodes = ELM_nodes),\n",
" 'RF' : RandomForestRegressor( n_estimators = n_est, min_samples_leaf = n_samples_leaf, max_features = RF_features, n_jobs = -1 ),\n",
"# 'MLP': MLPRegressor( hidden_layer_sizes = MLP_layers ),\n",
" 'SVM': SVR( C = SVM_paramC, kernel = 'rbf', gamma = SVM_gamma ),\n",
"# 'RF_unc' : Uncertainty( RandomForestRegressor( n_estimators = n_est, min_samples_leaf = n_samples_leaf, n_jobs = -1 ) ),\n",
"# 'ELM_unc': Uncertainty( ELM_Ensemble( n_estimators = ELM_est, n_nodes = ELM_nodes) )\n",
" }\n",
" return estimator\n",
"\n",
"# 'XGB': XGBRegressor( n_estimators = n_est, max_depth = tree_depth, n_jobs = -1 ),"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Check RMSE score"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"# choose model with which to perform the feature selection\n",
"model = 'RF'\n",
"training = training_data.sample(100000)\n",
"\n",
"MSE = pd.DataFrame(data = [], columns = ['mse', 'mse_norm', 'cv_time'])\n",
"N = len(feature_names)\n",
"\n",
"# get all all features\n",
"x, t, X, T = get_data( training, feature_names, label_name)\n",
"mse_df = pd.DataFrame(data = np.zeros((len(feature_names), 3)), \n",
" index = feature_names, columns = ['mse', 'mse_norm', 'cv_time'])\n",
"mse_df.loc['all', :] = get_mse( x, t, X, T, model )\n",
"MSE = MSE.append(mse_df.loc['all',:])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"np.sqrt(MSE)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# PERFORM MODEL TRAINING"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"training = training_data.sample(1000)\n",
"x, t, X, T = get_data( training, feature_names, label_name)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"testing = testing_data.sample(1000000)\n",
"x_tst, t_tst, X_tst, T_tst = get_data( testing, feature_names, label_name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Train with 1k samples"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Testing LIN\n",
"Training time: 0.001s\n",
"LIN MSE: 26982.883388\n",
"LIN RMSE: 164.264675\n",
"LIN MAE: 119.902816\n",
"LIN MAEP: 0.141090\n",
"LIN MBE: -65.246979\n",
"LIN MBEP: -0.088978\n",
"LIN R2: 0.507454\n",
"Prediction time: 0.018s\n",
"Testing KNN\n",
"Training time: 0.001s\n",
"KNN MSE: 21454.929649\n",
"KNN RMSE: 146.475014\n",
"KNN MAE: 105.105406\n",
"KNN MAEP: 0.125653\n",
"KNN MBE: -86.110776\n",
"KNN MBEP: -0.109208\n",
"KNN R2: 0.608362\n",
"Prediction time: 0.464s\n",
"Testing ELM\n",
"Covariance matrix is not full rank; solving with SVD (slow)\n",
"This happened because you have duplicated or too many neurons\n",
"Training time: 0.033s\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Applications/anaconda2/envs/py3/lib/python3.6/site-packages/hpelm/nnets/slfn_python.py:65: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
"To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
" B = np.linalg.lstsq(HH, HT)[0]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"ELM MSE: 15603.389089\n",
"ELM RMSE: 124.913526\n",
"ELM MAE: 88.024960\n",
"ELM MAEP: 0.101190\n",
"ELM MBE: -41.618954\n",
"ELM MBEP: -0.047334\n",
"ELM R2: 0.715176\n",
"Prediction time: 1.303s\n",
"Testing RF\n",
"Training time: 0.559s\n",
"RF MSE: 14532.584605\n",
"RF RMSE: 120.551170\n",
"RF MAE: 83.002565\n",
"RF MAEP: 0.102909\n",
"RF MBE: -28.941286\n",
"RF MBEP: -0.059140\n",
"RF R2: 0.734722\n",
"Prediction time: 1.045s\n",
"Testing SVM\n",
"Training time: 0.047s\n",
"SVM MSE: 14628.695956\n",
"SVM RMSE: 120.949146\n",
"SVM MAE: 85.957017\n",
"SVM MAEP: 0.096780\n",
"SVM MBE: -52.394696\n",
"SVM MBEP: -0.061283\n",
"SVM R2: 0.732968\n",
"Prediction time: 1.237s\n"
]
}
],
"source": [
"training = training_data.sample(1000)\n",
"x, t, X, T = get_data( training, feature_names, label_name)\n",
"\n",
"# names = ['RF']\n",
"# for name in names:\n",
"# est = set_estimators()[name]\n",
"for name, est in (set_estimators()).items():\n",
" print('Testing %s' %name)\n",
" tt = time.time()\n",
"\n",
" if name == 'RF':\n",
" est.fit(X, T)\n",
" print('Training time: %.3fs' %(time.time()-tt))\n",
" tt = time.time()\n",
" Y_tst = est.predict(X_tst)\n",
" \n",
" else:\n",
" est.fit(x, t)\n",
" print('Training time: %.3fs' %(time.time()-tt))\n",
" tt = time.time() \n",
" y_tst = est.predict(x_tst)\n",
" Y_tst = rescale(y_tst, T)\n",
"\n",
" pred_err = get_errors( shp(T_tst), shp(Y_tst), name )\n",
"\n",
" print('Prediction time: %.3fs' %(time.time()-tt))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Train with 10k samples"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Testing LIN\n",
"Training time: 0.002s\n",
"LIN MSE: 24299.728535\n",
"LIN RMSE: 155.883702\n",
"LIN MAE: 115.352831\n",
"LIN MAEP: 0.135507\n",
"LIN MBE: -56.588506\n",
"LIN MBEP: -0.080749\n",
"LIN R2: 0.556433\n",
"Prediction time: 0.011s\n",
"Testing KNN\n",
"Training time: 0.006s\n",
"KNN MSE: 14377.087552\n",
"KNN RMSE: 119.904493\n",
"KNN MAE: 83.957667\n",
"KNN MAEP: 0.097196\n",
"KNN MBE: -59.906368\n",
"KNN MBEP: -0.075650\n",
"KNN R2: 0.737561\n",
"Prediction time: 1.175s\n",
"Testing ELM\n",
"Training time: 0.248s\n",
"ELM MSE: 13826.241591\n",
"ELM RMSE: 117.585040\n",
"ELM MAE: 82.904287\n",
"ELM MAEP: 0.096043\n",
"ELM MBE: -38.329206\n",
"ELM MBEP: -0.044756\n",
"ELM R2: 0.747616\n",
"Prediction time: 1.373s\n",
"Testing RF\n",
"Training time: 3.715s\n",
"RF MSE: 10142.684488\n",
"RF RMSE: 100.710896\n",
"RF MAE: 65.392170\n",
"RF MAEP: 0.080950\n",
"RF MBE: -24.280610\n",
"RF MBEP: -0.046677\n",
"RF R2: 0.814855\n",
"Prediction time: 2.148s\n",
"Testing SVM\n",
"Training time: 3.077s\n",
"SVM MSE: 13383.093139\n",
"SVM RMSE: 115.685319\n",
"SVM MAE: 80.539995\n",
"SVM MAEP: 0.089857\n",
"SVM MBE: -47.797848\n",
"SVM MBEP: -0.056404\n",
"SVM R2: 0.755705\n",
"Prediction time: 10.935s\n"
]
}
],
"source": [
"training = training_data.sample(10000)\n",
"x, t, X, T = get_data( training, feature_names, label_name)\n",
"\n",
"# names = ['RF']\n",
"# for name in names:\n",
"# est = set_estimators()[name]\n",
"for name, est in (set_estimators()).items():\n",
" print('Testing %s' %name)\n",
" tt = time.time()\n",
"\n",
" if name == 'RF':\n",
" est.fit(X, T)\n",
" print('Training time: %.3fs' %(time.time()-tt))\n",
" tt = time.time()\n",
" Y_tst = est.predict(X_tst)\n",
" \n",
" else:\n",
" est.fit(x, t)\n",
" print('Training time: %.3fs' %(time.time()-tt))\n",
" tt = time.time() \n",
" y_tst = est.predict(x_tst)\n",
" Y_tst = rescale(y_tst, T)\n",
"\n",
" pred_err = get_errors( shp(T_tst), shp(Y_tst), name )\n",
"\n",
" print('Prediction time: %.3fs' %(time.time()-tt))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Train with 100k samples"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Testing LIN\n",
"Training time: 0.011s\n",
"LIN MSE: 24624.086460\n",
"LIN RMSE: 156.920637\n",
"LIN MAE: 115.704711\n",
"LIN MAEP: 0.140569\n",
"LIN MBE: -57.738216\n",
"LIN MBEP: -0.086214\n",
"LIN R2: 0.552164\n",
"Prediction time: 0.088s\n",
"Testing KNN\n",
"Training time: 0.126s\n",
"KNN MSE: 12893.554427\n",
"KNN RMSE: 113.549788\n",
"KNN MAE: 77.482022\n",
"KNN MAEP: 0.092345\n",
"KNN MBE: -48.087385\n",
"KNN MBEP: -0.064762\n",
"KNN R2: 0.765506\n",
"Prediction time: 19.820s\n",
"Testing ELM\n",
"Covariance matrix is not full rank; solving with SVD (slow)\n",
"This happened because you have duplicated or too many neurons\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Applications/anaconda2/envs/py3/lib/python3.6/site-packages/hpelm/nnets/slfn_python.py:65: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
"To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
" B = np.linalg.lstsq(HH, HT)[0]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Covariance matrix is not full rank; solving with SVD (slow)\n",
"This happened because you have duplicated or too many neurons\n",
"Covariance matrix is not full rank; solving with SVD (slow)\n",
"This happened because you have duplicated or too many neurons\n",
"Covariance matrix is not full rank; solving with SVD (slow)\n",
"This happened because you have duplicated or too many neurons\n",
"Covariance matrix is not full rank; solving with SVD (slow)\n",
"This happened because you have duplicated or too many neurons\n",
"Training time: 2.210s\n",
"ELM MSE: 13762.179344\n",
"ELM RMSE: 117.312315\n",
"ELM MAE: 82.044889\n",
"ELM MAEP: 0.099192\n",
"ELM MBE: -39.304112\n",
"ELM MBEP: -0.050386\n",
"ELM R2: 0.749709\n",
"Prediction time: 28.940s\n",
"Testing RF\n",
"Training time: 44.870s\n",
"RF MSE: 8913.686690\n",
"RF RMSE: 94.412323\n",
"RF MAE: 58.513073\n",
"RF MAEP: 0.075683\n",
"RF MBE: -14.097471\n",
"RF MBEP: -0.037031\n",
"RF R2: 0.837888\n",
"Prediction time: 30.668s\n",
"Testing SVM\n",
"Training time: 254.118s\n",
"SVM MSE: 12978.746841\n",
"SVM RMSE: 113.924303\n",
"SVM MAE: 78.730250\n",
"SVM MAEP: 0.092111\n",
"SVM MBE: -46.270215\n",
"SVM MBEP: -0.058881\n",
"SVM R2: 0.763957\n",
"Prediction time: 1178.289s\n"
]
}
],
"source": [
"training = training_data.sample(100000)\n",
"x, t, X, T = get_data( training, feature_names, label_name)\n",
"\n",
"# names = ['RF']\n",
"# for name in names:\n",
"# est = set_estimators()[name]\n",
"init = True\n",
"for name, est in (set_estimators()).items():\n",
" print('Testing %s' %name)\n",
" tt = time.time()\n",
"\n",
" if name == 'RF':\n",
" est.fit(X, T)\n",
" print('Training time: %.3fs' %(time.time()-tt))\n",
" tt = time.time()\n",
" Y_tst = est.predict(X_tst)\n",
" \n",
" else:\n",
" est.fit(x, t)\n",
" print('Training time: %.3fs' %(time.time()-tt))\n",
" tt = time.time() \n",
" y_tst = est.predict(x_tst)\n",
" Y_tst = rescale(y_tst, T)\n",
"\n",
" pred_err = get_errors( shp(T_tst), shp(Y_tst), name )\n",
"\n",
" print('Prediction time: %.3fs' %(time.time()-tt))\n",
" \n",
" if init: \n",
" init = False\n",
" continue\n",
" time.sleep(180)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"### RF and LIN are OK!\n",
"RF gives very good performance if X, T are used (no normalization)\n",
"LIN is in all cases not great, but functions as a reference model\n",
"\n",
"--> **for the other models, we should check different types of normalization, and with and without normalization**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### TUNING FOR KNN"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {},
"outputs": [],
"source": [
"training = training_data.sample(100000)\n",
"x, t, X, T = get_data( training, feature_names, label_name)"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"n_neighbors = 1\n",
"KNN (unscaled) MSE: 17936.489860\n",
"KNN (unscaled) RMSE: 133.927181\n",
"KNN (unscaled) MAE: 79.814240\n",
"KNN (unscaled) MAEP: 0.101270\n",
"KNN (unscaled) MBE: -7.217900\n",
"KNN (unscaled) MBEP: -0.038601\n",
"KNN (unscaled) R2: 0.712287\n",
"Crossvalidation time: 1.064s\n",
"KNN (scaled) MSE: 19677.763360\n",
"KNN (scaled) RMSE: 140.277451\n",
"KNN (scaled) MAE: 89.346540\n",
"KNN (scaled) MAEP: 0.106728\n",
"KNN (scaled) MBE: -3.728060\n",
"KNN (scaled) MBEP: -0.034882\n",
"KNN (scaled) R2: 0.684356\n",
"Crossvalidation time: 0.886s\n",
"\n",
"n_neighbors = 3\n",
"KNN (unscaled) MSE: 12194.562456\n",
"KNN (unscaled) RMSE: 110.428993\n",
"KNN (unscaled) MAE: 66.508287\n",
"KNN (unscaled) MAEP: 0.088468\n",
"KNN (unscaled) MBE: -9.034973\n",
"KNN (unscaled) MBEP: -0.040306\n",
"KNN (unscaled) R2: 0.804391\n",
"Crossvalidation time: 1.561s\n",
"KNN (scaled) MSE: 13216.161858\n",
"KNN (scaled) RMSE: 114.961567\n",
"KNN (scaled) MAE: 73.633820\n",
"KNN (scaled) MAEP: 0.092077\n",
"KNN (scaled) MBE: -4.446187\n",
"KNN (scaled) MBEP: -0.035396\n",
"KNN (scaled) R2: 0.788004\n",
"Crossvalidation time: 1.248s\n",
"\n",
"n_neighbors = 5\n",
"KNN (unscaled) MSE: 11099.403854\n",
"KNN (unscaled) RMSE: 105.353708\n",
"KNN (unscaled) MAE: 63.260656\n",
"KNN (unscaled) MAEP: 0.085720\n",
"KNN (unscaled) MBE: -10.136920\n",
"KNN (unscaled) MBEP: -0.041689\n",
"KNN (unscaled) R2: 0.821958\n",
"Crossvalidation time: 1.475s\n",
"KNN (scaled) MSE: 11955.351578\n",
"KNN (scaled) RMSE: 109.340530\n",
"KNN (scaled) MAE: 69.773316\n",
"KNN (scaled) MAEP: 0.088403\n",
"KNN (scaled) MBE: -5.201756\n",
"KNN (scaled) MBEP: -0.035990\n",
"KNN (scaled) R2: 0.808228\n",
"Crossvalidation time: 1.315s\n",
"\n",
"n_neighbors = 7\n",
"KNN (unscaled) MSE: 10706.984887\n",
"KNN (unscaled) RMSE: 103.474562\n",
"KNN (unscaled) MAE: 61.927021\n",
"KNN (unscaled) MAEP: 0.084790\n",
"KNN (unscaled) MBE: -10.867441\n",
"KNN (unscaled) MBEP: -0.042730\n",
"KNN (unscaled) R2: 0.828253\n",
"Crossvalidation time: 1.455s\n",
"KNN (scaled) MSE: 11392.805628\n",
"KNN (scaled) RMSE: 106.737086\n",
"KNN (scaled) MAE: 67.816767\n",
"KNN (scaled) MAEP: 0.086936\n",
"KNN (scaled) MBE: -5.736339\n",
"KNN (scaled) MBEP: -0.036814\n",
"KNN (scaled) R2: 0.817252\n",
"Crossvalidation time: 1.308s\n",
"\n",
"n_neighbors = 9\n",
"KNN (unscaled) MSE: 10543.600396\n",
"KNN (unscaled) RMSE: 102.682035\n",
"KNN (unscaled) MAE: 61.310733\n",
"KNN (unscaled) MAEP: 0.084391\n",
"KNN (unscaled) MBE: -11.532802\n",
"KNN (unscaled) MBEP: -0.043543\n",
"KNN (unscaled) R2: 0.830874\n",
"Crossvalidation time: 1.907s\n",
"KNN (scaled) MSE: 11132.352721\n",
"KNN (scaled) RMSE: 105.509965\n",
"KNN (scaled) MAE: 66.778321\n",
"KNN (scaled) MAEP: 0.086262\n",
"KNN (scaled) MBE: -6.243303\n",
"KNN (scaled) MBEP: -0.037596\n",
"KNN (scaled) R2: 0.821430\n",
"Crossvalidation time: 1.521s\n",
"\n",
"n_neighbors = 11\n",
"KNN (unscaled) MSE: 10452.999561\n",
"KNN (unscaled) RMSE: 102.239912\n",
"KNN (unscaled) MAE: 60.969124\n",
"KNN (unscaled) MAEP: 0.084270\n",
"KNN (unscaled) MBE: -12.089491\n",
"KNN (unscaled) MBEP: -0.044278\n",
"KNN (unscaled) R2: 0.832327\n",
"Crossvalidation time: 1.902s\n",
"KNN (scaled) MSE: 10975.228516\n",
"KNN (scaled) RMSE: 104.762725\n",
"KNN (scaled) MAE: 66.118878\n",
"KNN (scaled) MAEP: 0.085953\n",
"KNN (scaled) MBE: -6.554885\n",
"KNN (scaled) MBEP: -0.038208\n",
"KNN (scaled) R2: 0.823950\n",
"Crossvalidation time: 1.777s\n",
"\n",
"n_neighbors = 13\n",
"KNN (unscaled) MSE: 10416.797665\n",
"KNN (unscaled) RMSE: 102.062714\n",
"KNN (unscaled) MAE: 60.869478\n",
"KNN (unscaled) MAEP: 0.084387\n",
"KNN (unscaled) MBE: -12.464251\n",
"KNN (unscaled) MBEP: -0.044863\n",
"KNN (unscaled) R2: 0.832908\n",
"Crossvalidation time: 1.873s\n",
"KNN (scaled) MSE: 10864.128870\n",
"KNN (scaled) RMSE: 104.231132\n",
"KNN (scaled) MAE: 65.653434\n",
"KNN (scaled) MAEP: 0.085656\n",
"KNN (scaled) MBE: -6.987180\n",
"KNN (scaled) MBEP: -0.038707\n",
"KNN (scaled) R2: 0.825732\n",
"Crossvalidation time: 1.541s\n",
"\n",
"n_neighbors = 15\n",
"KNN (unscaled) MSE: 10407.979269\n",
"KNN (unscaled) RMSE: 102.019504\n",
"KNN (unscaled) MAE: 60.828119\n",
"KNN (unscaled) MAEP: 0.084519\n",
"KNN (unscaled) MBE: -12.905059\n",
"KNN (unscaled) MBEP: -0.045440\n",
"KNN (unscaled) R2: 0.833049\n",
"Crossvalidation time: 2.093s\n",
"KNN (scaled) MSE: 10790.107915\n",
"KNN (scaled) RMSE: 103.875444\n",
"KNN (scaled) MAE: 65.374872\n",
"KNN (scaled) MAEP: 0.085386\n",
"KNN (scaled) MBE: -7.334755\n",
"KNN (scaled) MBEP: -0.039021\n",
"KNN (scaled) R2: 0.826920\n",
"Crossvalidation time: 2.058s\n",
"\n",
"n_neighbors = 17\n",
"KNN (unscaled) MSE: 10404.359819\n",
"KNN (unscaled) RMSE: 102.001764\n",
"KNN (unscaled) MAE: 60.840614\n",
"KNN (unscaled) MAEP: 0.084702\n",
"KNN (unscaled) MBE: -13.303973\n",
"KNN (unscaled) MBEP: -0.045989\n",
"KNN (unscaled) R2: 0.833107\n",
"Crossvalidation time: 2.429s\n",
"KNN (scaled) MSE: 10750.128628\n",
"KNN (scaled) RMSE: 103.682827\n",
"KNN (scaled) MAE: 65.155901\n",
"KNN (scaled) MAEP: 0.085283\n",
"KNN (scaled) MBE: -7.616437\n",
"KNN (scaled) MBEP: -0.039390\n",
"KNN (scaled) R2: 0.827561\n",
"Crossvalidation time: 2.059s\n",
"\n",
"n_neighbors = 19\n",
"KNN (unscaled) MSE: 10411.482554\n",
"KNN (unscaled) RMSE: 102.036673\n",
"KNN (unscaled) MAE: 60.892743\n",
"KNN (unscaled) MAEP: 0.084887\n",
"KNN (unscaled) MBE: -13.650136\n",
"KNN (unscaled) MBEP: -0.046457\n",
"KNN (unscaled) R2: 0.832993\n",
"Crossvalidation time: 2.417s\n",
"KNN (scaled) MSE: 10714.865824\n",
"KNN (scaled) RMSE: 103.512636\n",
"KNN (scaled) MAE: 65.020369\n",
"KNN (scaled) MAEP: 0.085191\n",
"KNN (scaled) MBE: -7.860828\n",
"KNN (scaled) MBEP: -0.039656\n",
"KNN (scaled) R2: 0.828127\n",
"Crossvalidation time: 1.982s\n",
"\n",
"n_neighbors = 21\n",
"KNN (unscaled) MSE: 10433.962500\n",
"KNN (unscaled) RMSE: 102.146769\n",
"KNN (unscaled) MAE: 61.001188\n",
"KNN (unscaled) MAEP: 0.085100\n",
"KNN (unscaled) MBE: -13.958888\n",
"KNN (unscaled) MBEP: -0.046864\n",
"KNN (unscaled) R2: 0.832632\n",
"Crossvalidation time: 2.334s\n",
"KNN (scaled) MSE: 10702.347523\n",
"KNN (scaled) RMSE: 103.452151\n",
"KNN (scaled) MAE: 64.922793\n",
"KNN (scaled) MAEP: 0.085117\n",
"KNN (scaled) MBE: -8.073680\n",
"KNN (scaled) MBEP: -0.039885\n",
"KNN (scaled) R2: 0.828327\n",
"Crossvalidation time: 2.069s\n",
"\n",
"n_neighbors = 23\n",
"KNN (unscaled) MSE: 10456.314662\n",
"KNN (unscaled) RMSE: 102.256123\n",
"KNN (unscaled) MAE: 61.089005\n",
"KNN (unscaled) MAEP: 0.085257\n",
"KNN (unscaled) MBE: -14.256716\n",
"KNN (unscaled) MBEP: -0.047223\n",
"KNN (unscaled) R2: 0.832274\n",
"Crossvalidation time: 2.111s\n",
"KNN (scaled) MSE: 10689.113724\n",
"KNN (scaled) RMSE: 103.388170\n",
"KNN (scaled) MAE: 64.859814\n",
"KNN (scaled) MAEP: 0.085216\n",
"KNN (scaled) MBE: -8.331738\n",
"KNN (scaled) MBEP: -0.040285\n",
"KNN (scaled) R2: 0.828540\n",
"Crossvalidation time: 1.861s\n",
"\n",
"n_neighbors = 25\n",
"KNN (unscaled) MSE: 10490.688473\n",
"KNN (unscaled) RMSE: 102.424062\n",
"KNN (unscaled) MAE: 61.211516\n",
"KNN (unscaled) MAEP: 0.085473\n",
"KNN (unscaled) MBE: -14.551115\n",
"KNN (unscaled) MBEP: -0.047607\n",
"KNN (unscaled) R2: 0.831723\n",
"Crossvalidation time: 2.406s\n",
"KNN (scaled) MSE: 10680.215304\n",
"KNN (scaled) RMSE: 103.345127\n",
"KNN (scaled) MAE: 64.826315\n",
"KNN (scaled) MAEP: 0.085263\n",
"KNN (scaled) MBE: -8.536103\n",
"KNN (scaled) MBEP: -0.040559\n",
"KNN (scaled) R2: 0.828682\n",
"Crossvalidation time: 1.847s\n",
"\n",
"n_neighbors = 27\n",
"KNN (unscaled) MSE: 10519.643905\n",
"KNN (unscaled) RMSE: 102.565315\n",
"KNN (unscaled) MAE: 61.336524\n",
"KNN (unscaled) MAEP: 0.085675\n",
"KNN (unscaled) MBE: -14.801919\n",
"KNN (unscaled) MBEP: -0.047934\n",
"KNN (unscaled) R2: 0.831258\n",
"Crossvalidation time: 2.415s\n",
"KNN (scaled) MSE: 10680.828484\n",
"KNN (scaled) RMSE: 103.348094\n",
"KNN (scaled) MAE: 64.814751\n",
"KNN (scaled) MAEP: 0.085333\n",
"KNN (scaled) MBE: -8.750767\n",
"KNN (scaled) MBEP: -0.040840\n",
"KNN (scaled) R2: 0.828673\n",
"Crossvalidation time: 2.183s\n",
"\n",
"n_neighbors = 29\n",
"KNN (unscaled) MSE: 10551.474020\n",
"KNN (unscaled) RMSE: 102.720368\n",
"KNN (unscaled) MAE: 61.473164\n",
"KNN (unscaled) MAEP: 0.085902\n",
"KNN (unscaled) MBE: -15.027471\n",
"KNN (unscaled) MBEP: -0.048254\n",
"KNN (unscaled) R2: 0.830748\n",
"Crossvalidation time: 2.451s\n",
"KNN (scaled) MSE: 10674.096961\n",
"KNN (scaled) RMSE: 103.315521\n",
"KNN (scaled) MAE: 64.801763\n",
"KNN (scaled) MAEP: 0.085344\n",
"KNN (scaled) MBE: -8.936027\n",
"KNN (scaled) MBEP: -0.041038\n",
"KNN (scaled) R2: 0.828781\n",
"Crossvalidation time: 2.173s\n",
"\n",
"n_neighbors = 31\n",
"KNN (unscaled) MSE: 10586.091853\n",
"KNN (unscaled) RMSE: 102.888735\n",
"KNN (unscaled) MAE: 61.617209\n",
"KNN (unscaled) MAEP: 0.086129\n",
"KNN (unscaled) MBE: -15.234382\n",
"KNN (unscaled) MBEP: -0.048546\n",
"KNN (unscaled) R2: 0.830192\n",
"Crossvalidation time: 2.425s\n",
"KNN (scaled) MSE: 10676.314566\n",
"KNN (scaled) RMSE: 103.326253\n",
"KNN (scaled) MAE: 64.810894\n",
"KNN (scaled) MAEP: 0.085346\n",
"KNN (scaled) MBE: -9.148932\n",
"KNN (scaled) MBEP: -0.041235\n",
"KNN (scaled) R2: 0.828745\n",
"Crossvalidation time: 2.157s\n",
"\n",
"n_neighbors = 33\n",
"KNN (unscaled) MSE: 10617.628360\n",
"KNN (unscaled) RMSE: 103.041877\n",
"KNN (unscaled) MAE: 61.770765\n",
"KNN (unscaled) MAEP: 0.086325\n",
"KNN (unscaled) MBE: -15.443332\n",
"KNN (unscaled) MBEP: -0.048802\n",
"KNN (unscaled) R2: 0.829686\n",
"Crossvalidation time: 2.740s\n",
"KNN (scaled) MSE: 10673.333494\n",
"KNN (scaled) RMSE: 103.311826\n",
"KNN (scaled) MAE: 64.812968\n",
"KNN (scaled) MAEP: 0.085358\n",
"KNN (scaled) MBE: -9.315893\n",
"KNN (scaled) MBEP: -0.041402\n",
"KNN (scaled) R2: 0.828793\n",
"Crossvalidation time: 2.177s\n",
"\n",
"n_neighbors = 35\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"KNN (unscaled) MSE: 10640.251149\n",
"KNN (unscaled) RMSE: 103.151593\n",
"KNN (unscaled) MAE: 61.863727\n",
"KNN (unscaled) MAEP: 0.086469\n",
"KNN (unscaled) MBE: -15.634464\n",
"KNN (unscaled) MBEP: -0.049046\n",
"KNN (unscaled) R2: 0.829323\n",
"Crossvalidation time: 2.791s\n",
"KNN (scaled) MSE: 10682.688526\n",
"KNN (scaled) RMSE: 103.357092\n",
"KNN (scaled) MAE: 64.838960\n",
"KNN (scaled) MAEP: 0.085460\n",
"KNN (scaled) MBE: -9.495607\n",
"KNN (scaled) MBEP: -0.041644\n",
"KNN (scaled) R2: 0.828643\n",
"Crossvalidation time: 3.275s\n",
"\n",
"n_neighbors = 37\n",
"KNN (unscaled) MSE: 10670.352017\n",
"KNN (unscaled) RMSE: 103.297396\n",
"KNN (unscaled) MAE: 61.992450\n",
"KNN (unscaled) MAEP: 0.086629\n",
"KNN (unscaled) MBE: -15.831509\n",
"KNN (unscaled) MBEP: -0.049273\n",
"KNN (unscaled) R2: 0.828841\n",
"Crossvalidation time: 3.495s\n",
"KNN (scaled) MSE: 10684.478958\n",
"KNN (scaled) RMSE: 103.365753\n",
"KNN (scaled) MAE: 64.850631\n",
"KNN (scaled) MAEP: 0.085538\n",
"KNN (scaled) MBE: -9.651181\n",
"KNN (scaled) MBEP: -0.041856\n",
"KNN (scaled) R2: 0.828614\n",
"Crossvalidation time: 3.192s\n",
"\n",
"n_neighbors = 39\n",
"KNN (unscaled) MSE: 10707.069588\n",
"KNN (unscaled) RMSE: 103.474971\n",
"KNN (unscaled) MAE: 62.144333\n",
"KNN (unscaled) MAEP: 0.086804\n",
"KNN (unscaled) MBE: -16.008472\n",
"KNN (unscaled) MBEP: -0.049477\n",
"KNN (unscaled) R2: 0.828252\n",
"Crossvalidation time: 3.382s\n",
"KNN (scaled) MSE: 10698.132130\n",
"KNN (scaled) RMSE: 103.431775\n",
"KNN (scaled) MAE: 64.876131\n",
"KNN (scaled) MAEP: 0.085602\n",
"KNN (scaled) MBE: -9.795402\n",
"KNN (scaled) MBEP: -0.042030\n",
"KNN (scaled) R2: 0.828395\n",
"Crossvalidation time: 2.953s\n",
"\n",
"n_neighbors = 41\n",
"KNN (unscaled) MSE: 10738.284487\n",
"KNN (unscaled) RMSE: 103.625694\n",
"KNN (unscaled) MAE: 62.267558\n",
"KNN (unscaled) MAEP: 0.086970\n",
"KNN (unscaled) MBE: -16.181061\n",
"KNN (unscaled) MBEP: -0.049694\n",
"KNN (unscaled) R2: 0.827751\n",
"Crossvalidation time: 3.657s\n",
"KNN (scaled) MSE: 10701.172923\n",
"KNN (scaled) RMSE: 103.446474\n",
"KNN (scaled) MAE: 64.889298\n",
"KNN (scaled) MAEP: 0.085593\n",
"KNN (scaled) MBE: -9.947980\n",
"KNN (scaled) MBEP: -0.042149\n",
"KNN (scaled) R2: 0.828346\n",
"Crossvalidation time: 2.614s\n",
"\n",
"n_neighbors = 43\n",
"KNN (unscaled) MSE: 10769.089157\n",
"KNN (unscaled) RMSE: 103.774222\n",
"KNN (unscaled) MAE: 62.383713\n",
"KNN (unscaled) MAEP: 0.087140\n",
"KNN (unscaled) MBE: -16.344613\n",
"KNN (unscaled) MBEP: -0.049912\n",
"KNN (unscaled) R2: 0.827257\n",
"Crossvalidation time: 3.098s\n",
"KNN (scaled) MSE: 10710.505596\n",
"KNN (scaled) RMSE: 103.491573\n",
"KNN (scaled) MAE: 64.906860\n",
"KNN (scaled) MAEP: 0.085667\n",
"KNN (scaled) MBE: -10.090464\n",
"KNN (scaled) MBEP: -0.042341\n",
"KNN (scaled) R2: 0.828197\n",
"Crossvalidation time: 3.004s\n",
"\n",
"n_neighbors = 45\n",
"KNN (unscaled) MSE: 10797.895615\n",
"KNN (unscaled) RMSE: 103.912923\n",
"KNN (unscaled) MAE: 62.504908\n",
"KNN (unscaled) MAEP: 0.087299\n",
"KNN (unscaled) MBE: -16.491526\n",
"KNN (unscaled) MBEP: -0.050103\n",
"KNN (unscaled) R2: 0.826795\n",
"Crossvalidation time: 3.261s\n",
"KNN (scaled) MSE: 10719.709235\n",
"KNN (scaled) RMSE: 103.536029\n",
"KNN (scaled) MAE: 64.941426\n",
"KNN (scaled) MAEP: 0.085751\n",
"KNN (scaled) MBE: -10.221365\n",
"KNN (scaled) MBEP: -0.042518\n",
"KNN (scaled) R2: 0.828049\n",
"Crossvalidation time: 3.044s\n",
"\n",
"n_neighbors = 47\n",
"KNN (unscaled) MSE: 10825.161872\n",
"KNN (unscaled) RMSE: 104.044038\n",
"KNN (unscaled) MAE: 62.623741\n",
"KNN (unscaled) MAEP: 0.087450\n",
"KNN (unscaled) MBE: -16.631150\n",
"KNN (unscaled) MBEP: -0.050279\n",
"KNN (unscaled) R2: 0.826357\n",
"Crossvalidation time: 4.019s\n",
"KNN (scaled) MSE: 10732.175078\n",
"KNN (scaled) RMSE: 103.596212\n",
"KNN (scaled) MAE: 64.975693\n",
"KNN (scaled) MAEP: 0.085834\n",
"KNN (scaled) MBE: -10.364926\n",
"KNN (scaled) MBEP: -0.042703\n",
"KNN (scaled) R2: 0.827849\n",
"Crossvalidation time: 3.649s\n",
"\n",
"n_neighbors = 49\n",
"KNN (unscaled) MSE: 10857.319148\n",
"KNN (unscaled) RMSE: 104.198460\n",
"KNN (unscaled) MAE: 62.749351\n",
"KNN (unscaled) MAEP: 0.087658\n",
"KNN (unscaled) MBE: -16.774707\n",
"KNN (unscaled) MBEP: -0.050514\n",
"KNN (unscaled) R2: 0.825842\n",
"Crossvalidation time: 4.230s\n",
"KNN (scaled) MSE: 10742.371567\n",
"KNN (scaled) RMSE: 103.645413\n",
"KNN (scaled) MAE: 65.008273\n",
"KNN (scaled) MAEP: 0.085902\n",
"KNN (scaled) MBE: -10.489409\n",
"KNN (scaled) MBEP: -0.042856\n",
"KNN (scaled) R2: 0.827685\n",
"Crossvalidation time: 3.441s\n"
]
}
],
"source": [
"name = 'KNN'\n",
"knn_tuning = pd.DataFrame(data = [], columns = ['unscaled', 'scaled'])\n",
"\n",
"for n_neighbors in range(1,51, 2):\n",
" print('\\nn_neighbors = %d' %n_neighbors)\n",
" tt = time.time()\n",
"\n",
" est = KNeighborsRegressor( n_neighbors = n_neighbors, n_jobs = -1 )\n",
" Y = cross_val_predict(est, X, T)\n",
" unscaled_err = get_errors( shp(T), shp(Y), 'KNN (unscaled)' )\n",
" \n",
" print('Crossvalidation time: %.3fs' %(time.time()-tt))\n",
" tt = time.time()\n",
" \n",
" est = KNeighborsRegressor( n_neighbors = n_neighbors, n_jobs = -1 )\n",
" Y = cross_val_predict(est, x, T)\n",
" scaled_err = get_errors( shp(T), shp(Y), 'KNN (scaled)' )\n",
" \n",
" print('Crossvalidation time: %.3fs' %(time.time()-tt))\n",
" \n",
" knn_tuning = knn_tuning.append( pd.DataFrame(data = [[unscaled_err.RMSE, scaled_err.RMSE]], \n",
" columns = ['unscaled', 'scaled'], index = [n_neighbors]) )"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>unscaled</th>\n",
" <th>scaled</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>133.927181</td>\n",
" <td>140.277451</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>116.318220</td>\n",
" <td>121.763431</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>110.428993</td>\n",
" <td>114.961567</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>107.106472</td>\n",
" <td>111.392788</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>105.353708</td>\n",
" <td>109.340530</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>104.148116</td>\n",
" <td>107.898006</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>103.474562</td>\n",
" <td>106.737086</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>103.000060</td>\n",
" <td>106.043608</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>102.682035</td>\n",
" <td>105.509965</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>102.407954</td>\n",
" <td>105.045268</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>102.239912</td>\n",
" <td>104.762725</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>102.148309</td>\n",
" <td>104.457737</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>102.062714</td>\n",
" <td>104.231132</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>101.991796</td>\n",
" <td>104.074468</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>102.019504</td>\n",
" <td>103.875444</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>102.003360</td>\n",
" <td>103.749668</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>102.001764</td>\n",
" <td>103.682827</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>102.015503</td>\n",
" <td>103.582393</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>102.036673</td>\n",
" <td>103.512636</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>102.082279</td>\n",
" <td>103.468825</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>102.146769</td>\n",
" <td>103.452151</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>102.204693</td>\n",
" <td>103.388752</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>102.256123</td>\n",
" <td>103.388170</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>102.342830</td>\n",
" <td>103.339980</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>102.424062</td>\n",
" <td>103.345127</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>102.477265</td>\n",
" <td>103.340534</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>102.565315</td>\n",
" <td>103.348094</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>102.628012</td>\n",
" <td>103.343503</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>102.720368</td>\n",
" <td>103.315521</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>102.808029</td>\n",
" <td>103.318041</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" unscaled scaled\n",
"1 133.927181 140.277451\n",
"2 116.318220 121.763431\n",
"3 110.428993 114.961567\n",
"4 107.106472 111.392788\n",
"5 105.353708 109.340530\n",
"6 104.148116 107.898006\n",
"7 103.474562 106.737086\n",
"8 103.000060 106.043608\n",
"9 102.682035 105.509965\n",
"10 102.407954 105.045268\n",
"11 102.239912 104.762725\n",
"12 102.148309 104.457737\n",
"13 102.062714 104.231132\n",
"14 101.991796 104.074468\n",
"15 102.019504 103.875444\n",
"16 102.003360 103.749668\n",
"17 102.001764 103.682827\n",
"18 102.015503 103.582393\n",
"19 102.036673 103.512636\n",
"20 102.082279 103.468825\n",
"21 102.146769 103.452151\n",
"22 102.204693 103.388752\n",
"23 102.256123 103.388170\n",
"24 102.342830 103.339980\n",
"25 102.424062 103.345127\n",
"26 102.477265 103.340534\n",
"27 102.565315 103.348094\n",
"28 102.628012 103.343503\n",
"29 102.720368 103.315521\n",
"30 102.808029 103.318041"
]
},
"execution_count": 92,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"knn_tuning"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {},
"outputs": [],
"source": [
"knn_tuning.to_csv('KNN_tuning.csv')"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"unscaled 17\n",
"scaled 33\n",
"dtype: int64"
]
},
"execution_count": 95,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"knn_tuning.idxmin()"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {},
"outputs": [
{
"data": {
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" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"knn_tuning.unscaled.plot(lw = 2, label = 'RMSE for KNN')\n",
"plt.xlabel('Number of neighbors')\n",
"plt.ylabel('RMSE (in $kWh/m^2$)')\n",
"plt.legend()\n",
"plt.grid()\n",
"plt.tight_layout()\n",
"plt.savefig('KNN_tuning.png', dpi = 300)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Tuning ELM"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {},
"outputs": [],
"source": [
"training = training_data.sample(100000)\n",
"x, t, X, T = get_data( training, feature_names, label_name)"
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"ELM nodes = 50\n",
"ELM (unscaled) MSE: 10900.260481\n",
"ELM (unscaled) RMSE: 104.404313\n",
"ELM (unscaled) MAE: 66.563815\n",
"ELM (unscaled) MAEP: 0.083317\n",
"ELM (unscaled) MBE: -0.009370\n",
"ELM (unscaled) MBEP: -0.028423\n",
"ELM (unscaled) R2: 0.826046\n",
"Crossvalidation time: 8.801s\n",
"ELM (scaled) MSE: 10893.756707\n",
"ELM (scaled) RMSE: 104.373161\n",
"ELM (scaled) MAE: 66.552260\n",
"ELM (scaled) MAEP: 0.083305\n",
"ELM (scaled) MBE: -0.028816\n",
"ELM (scaled) MBEP: -0.028458\n",
"ELM (scaled) R2: 0.826149\n",
"Crossvalidation time: 8.094s\n"
]
}
],
"source": [
"name = 'ELM'\n",
"ELM_est = 25\n",
"#elm_tuning = pd.DataFrame(data = [], columns = ['unscaled_target', 'scaled_target'])\n",
"\n",
"for ELM_nodes in [50]:\n",
" print('\\nELM nodes = %d' %ELM_nodes)\n",
" tt = time.time()\n",
"\n",
" est = ELM_Ensemble( n_estimators = ELM_est, n_nodes = ELM_nodes )\n",
" Y = cross_val_predict(est, x, T)\n",
" unscaled_err = get_errors( shp(T), shp(Y), 'ELM (unscaled)' )\n",
" \n",
" print('Crossvalidation time: %.3fs' %(time.time()-tt))\n",
" tt = time.time()\n",
" \n",
" est = ELM_Ensemble( n_estimators = ELM_est, n_nodes = ELM_nodes )\n",
" y = cross_val_predict(est, x, t)\n",
" Y = rescale(y, T)\n",
" scaled_err = get_errors( shp(T), shp(Y), 'ELM (scaled)' )\n",
" \n",
" print('Crossvalidation time: %.3fs' %(time.time()-tt))\n",
" \n",
" if (unscaled_err.RMSE > 200) & (ELM_nodes > 20): break\n",
" \n",
" #elm_tuning = elm_tuning.append( pd.DataFrame(data = [[unscaled_err.RMSE, scaled_err.RMSE]], \n",
" #columns = ['unscaled_target', 'scaled_target'], index = [ELM_nodes]) )"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {},
"outputs": [
{
"data": {
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" <thead>\n",
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" <th></th>\n",
" <th>unscaled_target</th>\n",
" <th>scaled_target</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>252.056856</td>\n",
" <td>250.313907</td>\n",
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" <th>6</th>\n",
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" <td>163.030005</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>143.353876</td>\n",
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" <td>121.154071</td>\n",
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" <tr>\n",
" <th>41</th>\n",
" <td>105.774135</td>\n",
" <td>106.085332</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>104.854574</td>\n",
" <td>104.940364</td>\n",
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" <tr>\n",
" <th>51</th>\n",
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" <th>56</th>\n",
" <td>103.623092</td>\n",
" <td>103.828793</td>\n",
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" <td>103.281091</td>\n",
" </tr>\n",
" <tr>\n",
" <th>66</th>\n",
" <td>104.027258</td>\n",
" <td>103.132682</td>\n",
" </tr>\n",
" <tr>\n",
" <th>71</th>\n",
" <td>103.298952</td>\n",
" <td>103.221512</td>\n",
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" <tr>\n",
" <th>76</th>\n",
" <td>103.216895</td>\n",
" <td>103.139736</td>\n",
" </tr>\n",
" <tr>\n",
" <th>81</th>\n",
" <td>103.096608</td>\n",
" <td>103.087989</td>\n",
" </tr>\n",
" <tr>\n",
" <th>86</th>\n",
" <td>103.031711</td>\n",
" <td>103.017617</td>\n",
" </tr>\n",
" <tr>\n",
" <th>91</th>\n",
" <td>103.022977</td>\n",
" <td>102.760905</td>\n",
" </tr>\n",
" <tr>\n",
" <th>96</th>\n",
" <td>102.942882</td>\n",
" <td>102.889443</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" unscaled_target scaled_target\n",
"1 252.056856 250.313907\n",
"6 158.953529 163.030005\n",
"11 143.353876 143.447775\n",
"16 133.347560 133.440524\n",
"21 120.861807 121.154071\n",
"26 111.439309 113.364057\n",
"31 108.308805 108.561483\n",
"36 107.089450 107.018143\n",
"41 105.774135 106.085332\n",
"46 104.854574 104.940364\n",
"51 104.366426 104.161843\n",
"56 103.623092 103.828793\n",
"61 103.364507 103.281091\n",
"66 104.027258 103.132682\n",
"71 103.298952 103.221512\n",
"76 103.216895 103.139736\n",
"81 103.096608 103.087989\n",
"86 103.031711 103.017617\n",
"91 103.022977 102.760905\n",
"96 102.942882 102.889443"
]
},
"execution_count": 118,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"elm_tuning"
]
},
{
"cell_type": "code",
"execution_count": 129,
"metadata": {},
"outputs": [],
"source": [
"elm_tuning.to_csv('ELM_tuning_n25.csv')"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {},
"outputs": [
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" if (mpl.ratio != 1) {\n",
" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
" }\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" fig.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"elm_tuning.unscaled_target.plot(lw = 2, label = 'RMSE for ELM')\n",
"plt.plot([50, 50], [0, 300], '--', label = 'Lowest RMSE with full rank')\n",
"plt.xlabel('Number of neurons')\n",
"plt.ylabel('RMSE (in $kWh/m^2$)')\n",
"plt.ylim((90, 250))\n",
"plt.legend()\n",
"plt.grid()\n",
"plt.tight_layout()\n",
"plt.savefig('ELM_tuning.png', dpi = 300)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Tuning SVM"
]
},
{
"cell_type": "code",
"execution_count": 134,
"metadata": {},
"outputs": [],
"source": [
"training = training_data.sample(1000)\n",
"x, t, X, T = get_data( training, feature_names, label_name)"
]
},
{
"cell_type": "code",
"execution_count": 135,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"SVM C = 1.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 10.000000\n",
"SVM (scaled) MSE: 20257.683947\n",
"SVM (scaled) RMSE: 142.329491\n",
"SVM (scaled) MAE: 100.803742\n",
"SVM (scaled) MAEP: 0.121330\n",
"SVM (scaled) MBE: -13.588861\n",
"SVM (scaled) MBEP: -0.051479\n",
"SVM (scaled) R2: 0.680607\n",
"Crossvalidation time: 18.183s\n",
"\n",
"SVM C = 1.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 20.000000\n",
"SVM (scaled) MSE: 15193.700803\n",
"SVM (scaled) RMSE: 123.262731\n",
"SVM (scaled) MAE: 83.405053\n",
"SVM (scaled) MAEP: 0.102458\n",
"SVM (scaled) MBE: -12.101577\n",
"SVM (scaled) MBEP: -0.046295\n",
"SVM (scaled) R2: 0.760448\n",
"Crossvalidation time: 19.089s\n",
"\n",
"SVM C = 1.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 50.000000\n",
"SVM (scaled) MSE: 11746.300776\n",
"SVM (scaled) RMSE: 108.380352\n",
"SVM (scaled) MAE: 67.392852\n",
"SVM (scaled) MAEP: 0.085151\n",
"SVM (scaled) MBE: -12.211091\n",
"SVM (scaled) MBEP: -0.041217\n",
"SVM (scaled) R2: 0.814802\n",
"Crossvalidation time: 17.983s\n",
"\n",
"SVM C = 1.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 70.000000\n",
"SVM (scaled) MSE: 11410.437945\n",
"SVM (scaled) RMSE: 106.819651\n",
"SVM (scaled) MAE: 65.866525\n",
"SVM (scaled) MAEP: 0.083401\n",
"SVM (scaled) MBE: -12.158720\n",
"SVM (scaled) MBEP: -0.040521\n",
"SVM (scaled) R2: 0.820097\n",
"Crossvalidation time: 17.730s\n",
"\n",
"SVM C = 1.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 100.000000\n",
"SVM (scaled) MSE: 11218.841350\n",
"SVM (scaled) RMSE: 105.919032\n",
"SVM (scaled) MAE: 65.024270\n",
"SVM (scaled) MAEP: 0.082461\n",
"SVM (scaled) MBE: -12.778295\n",
"SVM (scaled) MBEP: -0.040685\n",
"SVM (scaled) R2: 0.823118\n",
"Crossvalidation time: 17.990s\n",
"\n",
"SVM C = 1.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 200.000000\n",
"SVM (scaled) MSE: 10955.732551\n",
"SVM (scaled) RMSE: 104.669635\n",
"SVM (scaled) MAE: 64.196585\n",
"SVM (scaled) MAEP: 0.081341\n",
"SVM (scaled) MBE: -12.761114\n",
"SVM (scaled) MBEP: -0.040025\n",
"SVM (scaled) R2: 0.827266\n",
"Crossvalidation time: 18.211s\n",
"\n",
"SVM C = 1.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 500.000000\n",
"SVM (scaled) MSE: 10871.185745\n",
"SVM (scaled) RMSE: 104.264979\n",
"SVM (scaled) MAE: 63.729139\n",
"SVM (scaled) MAEP: 0.080857\n",
"SVM (scaled) MBE: -12.595900\n",
"SVM (scaled) MBEP: -0.039674\n",
"SVM (scaled) R2: 0.828599\n",
"Crossvalidation time: 18.925s\n",
"\n",
"SVM C = 1.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 700.000000\n",
"SVM (scaled) MSE: 10900.085283\n",
"SVM (scaled) RMSE: 104.403474\n",
"SVM (scaled) MAE: 63.815915\n",
"SVM (scaled) MAEP: 0.080894\n",
"SVM (scaled) MBE: -12.388687\n",
"SVM (scaled) MBEP: -0.039405\n",
"SVM (scaled) R2: 0.828144\n",
"Crossvalidation time: 19.250s\n",
"\n",
"SVM C = 1.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 1000.000000\n",
"SVM (scaled) MSE: 10997.590351\n",
"SVM (scaled) RMSE: 104.869397\n",
"SVM (scaled) MAE: 64.068174\n",
"SVM (scaled) MAEP: 0.081323\n",
"SVM (scaled) MBE: -12.164438\n",
"SVM (scaled) MBEP: -0.039380\n",
"SVM (scaled) R2: 0.826606\n",
"Crossvalidation time: 20.219s\n",
"\n",
"SVM C = 2.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 10.000000\n",
"SVM (scaled) MSE: 18756.129847\n",
"SVM (scaled) RMSE: 136.953021\n",
"SVM (scaled) MAE: 96.394078\n",
"SVM (scaled) MAEP: 0.116256\n",
"SVM (scaled) MBE: -12.296302\n",
"SVM (scaled) MBEP: -0.049013\n",
"SVM (scaled) R2: 0.704281\n",
"Crossvalidation time: 18.101s\n",
"\n",
"SVM C = 2.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 20.000000\n",
"SVM (scaled) MSE: 13449.226202\n",
"SVM (scaled) RMSE: 115.970799\n",
"SVM (scaled) MAE: 75.881400\n",
"SVM (scaled) MAEP: 0.094216\n",
"SVM (scaled) MBE: -11.562613\n",
"SVM (scaled) MBEP: -0.043459\n",
"SVM (scaled) R2: 0.787952\n",
"Crossvalidation time: 18.108s\n",
"\n",
"SVM C = 2.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 50.000000\n",
"SVM (scaled) MSE: 11489.376738\n",
"SVM (scaled) RMSE: 107.188510\n",
"SVM (scaled) MAE: 66.243476\n",
"SVM (scaled) MAEP: 0.083769\n",
"SVM (scaled) MBE: -12.019165\n",
"SVM (scaled) MBEP: -0.040263\n",
"SVM (scaled) R2: 0.818852\n",
"Crossvalidation time: 18.338s\n",
"\n",
"SVM C = 2.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 70.000000\n",
"SVM (scaled) MSE: 11261.467602\n",
"SVM (scaled) RMSE: 106.120062\n",
"SVM (scaled) MAE: 65.210966\n",
"SVM (scaled) MAEP: 0.082578\n",
"SVM (scaled) MBE: -12.348857\n",
"SVM (scaled) MBEP: -0.040116\n",
"SVM (scaled) R2: 0.822446\n",
"Crossvalidation time: 18.478s\n",
"\n",
"SVM C = 2.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 100.000000\n",
"SVM (scaled) MSE: 11114.131903\n",
"SVM (scaled) RMSE: 105.423583\n",
"SVM (scaled) MAE: 64.690053\n",
"SVM (scaled) MAEP: 0.081927\n",
"SVM (scaled) MBE: -12.662760\n",
"SVM (scaled) MBEP: -0.040075\n",
"SVM (scaled) R2: 0.824769\n",
"Crossvalidation time: 18.350s\n",
"\n",
"SVM C = 2.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 200.000000\n",
"SVM (scaled) MSE: 10880.251537\n",
"SVM (scaled) RMSE: 104.308444\n",
"SVM (scaled) MAE: 63.914490\n",
"SVM (scaled) MAEP: 0.080816\n",
"SVM (scaled) MBE: -12.406063\n",
"SVM (scaled) MBEP: -0.039243\n",
"SVM (scaled) R2: 0.828456\n",
"Crossvalidation time: 18.815s\n",
"\n",
"SVM C = 2.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 500.000000\n",
"SVM (scaled) MSE: 10896.508972\n",
"SVM (scaled) RMSE: 104.386345\n",
"SVM (scaled) MAE: 63.778081\n",
"SVM (scaled) MAEP: 0.080708\n",
"SVM (scaled) MBE: -12.424258\n",
"SVM (scaled) MBEP: -0.039168\n",
"SVM (scaled) R2: 0.828200\n",
"Crossvalidation time: 19.913s\n",
"\n",
"SVM C = 2.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 700.000000\n",
"SVM (scaled) MSE: 10949.589861\n",
"SVM (scaled) RMSE: 104.640288\n",
"SVM (scaled) MAE: 63.921499\n",
"SVM (scaled) MAEP: 0.080892\n",
"SVM (scaled) MBE: -12.261219\n",
"SVM (scaled) MBEP: -0.039032\n",
"SVM (scaled) R2: 0.827363\n",
"Crossvalidation time: 21.399s\n",
"\n",
"SVM C = 2.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 1000.000000\n",
"SVM (scaled) MSE: 11103.099060\n",
"SVM (scaled) RMSE: 105.371244\n",
"SVM (scaled) MAE: 64.327001\n",
"SVM (scaled) MAEP: 0.081441\n",
"SVM (scaled) MBE: -12.082502\n",
"SVM (scaled) MBEP: -0.038945\n",
"SVM (scaled) R2: 0.824943\n",
"Crossvalidation time: 22.483s\n",
"\n",
"SVM C = 5.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 10.000000\n",
"SVM (scaled) MSE: 16002.534898\n",
"SVM (scaled) RMSE: 126.501126\n",
"SVM (scaled) MAE: 87.091312\n",
"SVM (scaled) MAEP: 0.105966\n",
"SVM (scaled) MBE: -10.789220\n",
"SVM (scaled) MBEP: -0.045364\n",
"SVM (scaled) R2: 0.747696\n",
"Crossvalidation time: 18.660s\n",
"\n",
"SVM C = 5.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 20.000000\n",
"SVM (scaled) MSE: 12319.303118\n",
"SVM (scaled) RMSE: 110.992356\n",
"SVM (scaled) MAE: 70.486338\n",
"SVM (scaled) MAEP: 0.088253\n",
"SVM (scaled) MBE: -10.934226\n",
"SVM (scaled) MBEP: -0.040516\n",
"SVM (scaled) R2: 0.805767\n",
"Crossvalidation time: 18.500s\n",
"\n",
"SVM C = 5.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 50.000000\n",
"SVM (scaled) MSE: 11287.069099\n",
"SVM (scaled) RMSE: 106.240619\n",
"SVM (scaled) MAE: 65.350242\n",
"SVM (scaled) MAEP: 0.082644\n",
"SVM (scaled) MBE: -12.069043\n",
"SVM (scaled) MBEP: -0.039582\n",
"SVM (scaled) R2: 0.822042\n",
"Crossvalidation time: 19.377s\n",
"\n",
"SVM C = 5.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 70.000000\n",
"SVM (scaled) MSE: 11144.603683\n",
"SVM (scaled) RMSE: 105.568005\n",
"SVM (scaled) MAE: 64.766358\n",
"SVM (scaled) MAEP: 0.081941\n",
"SVM (scaled) MBE: -12.425626\n",
"SVM (scaled) MBEP: -0.039652\n",
"SVM (scaled) R2: 0.824288\n",
"Crossvalidation time: 19.703s\n",
"\n",
"SVM C = 5.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 100.000000\n",
"SVM (scaled) MSE: 11015.898955\n",
"SVM (scaled) RMSE: 104.956653\n",
"SVM (scaled) MAE: 64.415911\n",
"SVM (scaled) MAEP: 0.081412\n",
"SVM (scaled) MBE: -12.371081\n",
"SVM (scaled) MBEP: -0.039261\n",
"SVM (scaled) R2: 0.826318\n",
"Crossvalidation time: 19.494s\n",
"\n",
"SVM C = 5.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 200.000000\n",
"SVM (scaled) MSE: 10853.234413\n",
"SVM (scaled) RMSE: 104.178858\n",
"SVM (scaled) MAE: 63.757718\n",
"SVM (scaled) MAEP: 0.080568\n",
"SVM (scaled) MBE: -12.265108\n",
"SVM (scaled) MBEP: -0.038787\n",
"SVM (scaled) R2: 0.828882\n",
"Crossvalidation time: 20.875s\n",
"\n",
"SVM C = 5.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 500.000000\n",
"SVM (scaled) MSE: 10978.580340\n",
"SVM (scaled) RMSE: 104.778721\n",
"SVM (scaled) MAE: 63.978085\n",
"SVM (scaled) MAEP: 0.080825\n",
"SVM (scaled) MBE: -12.291847\n",
"SVM (scaled) MBEP: -0.038792\n",
"SVM (scaled) R2: 0.826906\n",
"Crossvalidation time: 24.319s\n",
"\n",
"SVM C = 5.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 700.000000\n",
"SVM (scaled) MSE: 11081.028008\n",
"SVM (scaled) RMSE: 105.266462\n",
"SVM (scaled) MAE: 64.200320\n",
"SVM (scaled) MAEP: 0.081169\n",
"SVM (scaled) MBE: -12.081926\n",
"SVM (scaled) MBEP: -0.038681\n",
"SVM (scaled) R2: 0.825291\n",
"Crossvalidation time: 25.846s\n",
"\n",
"SVM C = 5.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 1000.000000\n",
"SVM (scaled) MSE: 11330.884157\n",
"SVM (scaled) RMSE: 106.446626\n",
"SVM (scaled) MAE: 64.902558\n",
"SVM (scaled) MAEP: 0.082015\n",
"SVM (scaled) MBE: -11.788053\n",
"SVM (scaled) MBEP: -0.038528\n",
"SVM (scaled) R2: 0.821351\n",
"Crossvalidation time: 28.581s\n",
"\n",
"SVM C = 7.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 10.000000\n",
"SVM (scaled) MSE: 14934.388753\n",
"SVM (scaled) RMSE: 122.206337\n",
"SVM (scaled) MAE: 83.043542\n",
"SVM (scaled) MAEP: 0.101501\n",
"SVM (scaled) MBE: -10.289837\n",
"SVM (scaled) MBEP: -0.043728\n",
"SVM (scaled) R2: 0.764537\n",
"Crossvalidation time: 18.738s\n",
"\n",
"SVM C = 7.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 20.000000\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"SVM (scaled) MSE: 12129.669095\n",
"SVM (scaled) RMSE: 110.134777\n",
"SVM (scaled) MAE: 69.492688\n",
"SVM (scaled) MAEP: 0.087226\n",
"SVM (scaled) MBE: -11.037482\n",
"SVM (scaled) MBEP: -0.040086\n",
"SVM (scaled) R2: 0.808757\n",
"Crossvalidation time: 18.214s\n",
"\n",
"SVM C = 7.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 50.000000\n",
"SVM (scaled) MSE: 11236.100437\n",
"SVM (scaled) RMSE: 106.000474\n",
"SVM (scaled) MAE: 65.136989\n",
"SVM (scaled) MAEP: 0.082355\n",
"SVM (scaled) MBE: -12.159941\n",
"SVM (scaled) MBEP: -0.039473\n",
"SVM (scaled) R2: 0.822846\n",
"Crossvalidation time: 19.684s\n",
"\n",
"SVM C = 7.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 70.000000\n",
"SVM (scaled) MSE: 11108.120925\n",
"SVM (scaled) RMSE: 105.395071\n",
"SVM (scaled) MAE: 64.667407\n",
"SVM (scaled) MAEP: 0.081771\n",
"SVM (scaled) MBE: -12.378668\n",
"SVM (scaled) MBEP: -0.039448\n",
"SVM (scaled) R2: 0.824864\n",
"Crossvalidation time: 19.683s\n",
"\n",
"SVM C = 7.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 100.000000\n",
"SVM (scaled) MSE: 10989.908051\n",
"SVM (scaled) RMSE: 104.832762\n",
"SVM (scaled) MAE: 64.336489\n",
"SVM (scaled) MAEP: 0.081255\n",
"SVM (scaled) MBE: -12.301090\n",
"SVM (scaled) MBEP: -0.039055\n",
"SVM (scaled) R2: 0.826727\n",
"Crossvalidation time: 20.913s\n",
"\n",
"SVM C = 7.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 200.000000\n",
"SVM (scaled) MSE: 10855.952581\n",
"SVM (scaled) RMSE: 104.191903\n",
"SVM (scaled) MAE: 63.750920\n",
"SVM (scaled) MAEP: 0.080572\n",
"SVM (scaled) MBE: -12.381558\n",
"SVM (scaled) MBEP: -0.038849\n",
"SVM (scaled) R2: 0.828839\n",
"Crossvalidation time: 21.698s\n",
"\n",
"SVM C = 7.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 500.000000\n",
"SVM (scaled) MSE: 11006.280387\n",
"SVM (scaled) RMSE: 104.910821\n",
"SVM (scaled) MAE: 64.039671\n",
"SVM (scaled) MAEP: 0.080897\n",
"SVM (scaled) MBE: -12.190828\n",
"SVM (scaled) MBEP: -0.038677\n",
"SVM (scaled) R2: 0.826469\n",
"Crossvalidation time: 25.777s\n",
"\n",
"SVM C = 7.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 700.000000\n",
"SVM (scaled) MSE: 11152.615175\n",
"SVM (scaled) RMSE: 105.605943\n",
"SVM (scaled) MAE: 64.374199\n",
"SVM (scaled) MAEP: 0.081394\n",
"SVM (scaled) MBE: -12.002048\n",
"SVM (scaled) MBEP: -0.038617\n",
"SVM (scaled) R2: 0.824162\n",
"Crossvalidation time: 28.799s\n",
"\n",
"SVM C = 7.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 1000.000000\n",
"SVM (scaled) MSE: 11464.371474\n",
"SVM (scaled) RMSE: 107.071805\n",
"SVM (scaled) MAE: 65.272678\n",
"SVM (scaled) MAEP: 0.082381\n",
"SVM (scaled) MBE: -11.589083\n",
"SVM (scaled) MBEP: -0.038318\n",
"SVM (scaled) R2: 0.819247\n",
"Crossvalidation time: 31.566s\n",
"\n",
"SVM C = 10.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 10.000000\n",
"SVM (scaled) MSE: 13978.946492\n",
"SVM (scaled) RMSE: 118.232595\n",
"SVM (scaled) MAE: 78.952758\n",
"SVM (scaled) MAEP: 0.097091\n",
"SVM (scaled) MBE: -10.224882\n",
"SVM (scaled) MBEP: -0.042507\n",
"SVM (scaled) R2: 0.779601\n",
"Crossvalidation time: 19.067s\n",
"\n",
"SVM C = 10.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 20.000000\n",
"SVM (scaled) MSE: 11983.580063\n",
"SVM (scaled) RMSE: 109.469539\n",
"SVM (scaled) MAE: 68.722585\n",
"SVM (scaled) MAEP: 0.086436\n",
"SVM (scaled) MBE: -11.029818\n",
"SVM (scaled) MBEP: -0.039606\n",
"SVM (scaled) R2: 0.811061\n",
"Crossvalidation time: 19.229s\n",
"\n",
"SVM C = 10.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 50.000000\n",
"SVM (scaled) MSE: 11193.517967\n",
"SVM (scaled) RMSE: 105.799423\n",
"SVM (scaled) MAE: 64.941255\n",
"SVM (scaled) MAEP: 0.082122\n",
"SVM (scaled) MBE: -12.328501\n",
"SVM (scaled) MBEP: -0.039508\n",
"SVM (scaled) R2: 0.823517\n",
"Crossvalidation time: 19.750s\n",
"\n",
"SVM C = 10.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 70.000000\n",
"SVM (scaled) MSE: 11077.825807\n",
"SVM (scaled) RMSE: 105.251251\n",
"SVM (scaled) MAE: 64.586028\n",
"SVM (scaled) MAEP: 0.081621\n",
"SVM (scaled) MBE: -12.278741\n",
"SVM (scaled) MBEP: -0.039219\n",
"SVM (scaled) R2: 0.825341\n",
"Crossvalidation time: 20.556s\n",
"\n",
"SVM C = 10.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 100.000000\n",
"SVM (scaled) MSE: 10955.084874\n",
"SVM (scaled) RMSE: 104.666541\n",
"SVM (scaled) MAE: 64.226135\n",
"SVM (scaled) MAEP: 0.081062\n",
"SVM (scaled) MBE: -12.097474\n",
"SVM (scaled) MBEP: -0.038725\n",
"SVM (scaled) R2: 0.827276\n",
"Crossvalidation time: 21.428s\n",
"\n",
"SVM C = 10.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 200.000000\n",
"SVM (scaled) MSE: 10858.283754\n",
"SVM (scaled) RMSE: 104.203089\n",
"SVM (scaled) MAE: 63.731179\n",
"SVM (scaled) MAEP: 0.080561\n",
"SVM (scaled) MBE: -12.401180\n",
"SVM (scaled) MBEP: -0.038840\n",
"SVM (scaled) R2: 0.828803\n",
"Crossvalidation time: 23.484s\n",
"\n",
"SVM C = 10.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 500.000000\n",
"SVM (scaled) MSE: 11046.318140\n",
"SVM (scaled) RMSE: 105.101466\n",
"SVM (scaled) MAE: 64.130753\n",
"SVM (scaled) MAEP: 0.081025\n",
"SVM (scaled) MBE: -12.141699\n",
"SVM (scaled) MBEP: -0.038638\n",
"SVM (scaled) R2: 0.825838\n",
"Crossvalidation time: 29.802s\n",
"\n",
"SVM C = 10.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 700.000000\n",
"SVM (scaled) MSE: 11246.886531\n",
"SVM (scaled) RMSE: 106.051339\n",
"SVM (scaled) MAE: 64.627563\n",
"SVM (scaled) MAEP: 0.081683\n",
"SVM (scaled) MBE: -11.894048\n",
"SVM (scaled) MBEP: -0.038507\n",
"SVM (scaled) R2: 0.822676\n",
"Crossvalidation time: 32.396s\n",
"\n",
"SVM C = 10.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 1000.000000\n",
"SVM (scaled) MSE: 11634.989322\n",
"SVM (scaled) RMSE: 107.865608\n",
"SVM (scaled) MAE: 65.722988\n",
"SVM (scaled) MAEP: 0.082859\n",
"SVM (scaled) MBE: -11.392820\n",
"SVM (scaled) MBEP: -0.038101\n",
"SVM (scaled) R2: 0.816557\n",
"Crossvalidation time: 38.182s\n",
"\n",
"SVM C = 20.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 10.000000\n",
"SVM (scaled) MSE: 12839.277351\n",
"SVM (scaled) RMSE: 113.310535\n",
"SVM (scaled) MAE: 73.470565\n",
"SVM (scaled) MAEP: 0.091257\n",
"SVM (scaled) MBE: -10.021826\n",
"SVM (scaled) MBEP: -0.040412\n",
"SVM (scaled) R2: 0.797569\n",
"Crossvalidation time: 19.364s\n",
"\n",
"SVM C = 20.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 20.000000\n",
"SVM (scaled) MSE: 11778.742220\n",
"SVM (scaled) RMSE: 108.529914\n",
"SVM (scaled) MAE: 67.704185\n",
"SVM (scaled) MAEP: 0.085323\n",
"SVM (scaled) MBE: -11.054244\n",
"SVM (scaled) MBEP: -0.038931\n",
"SVM (scaled) R2: 0.814290\n",
"Crossvalidation time: 20.162s\n",
"\n",
"SVM C = 20.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 50.000000\n",
"SVM (scaled) MSE: 11131.443253\n",
"SVM (scaled) RMSE: 105.505655\n",
"SVM (scaled) MAE: 64.736145\n",
"SVM (scaled) MAEP: 0.081800\n",
"SVM (scaled) MBE: -12.242910\n",
"SVM (scaled) MBEP: -0.039162\n",
"SVM (scaled) R2: 0.824496\n",
"Crossvalidation time: 22.115s\n",
"\n",
"SVM C = 20.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 70.000000\n",
"SVM (scaled) MSE: 11029.249914\n",
"SVM (scaled) RMSE: 105.020236\n",
"SVM (scaled) MAE: 64.446006\n",
"SVM (scaled) MAEP: 0.081392\n",
"SVM (scaled) MBE: -12.148036\n",
"SVM (scaled) MBEP: -0.038886\n",
"SVM (scaled) R2: 0.826107\n",
"Crossvalidation time: 22.964s\n",
"\n",
"SVM C = 20.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 100.000000\n",
"SVM (scaled) MSE: 10908.798791\n",
"SVM (scaled) RMSE: 104.445195\n",
"SVM (scaled) MAE: 64.032250\n",
"SVM (scaled) MAEP: 0.080822\n",
"SVM (scaled) MBE: -12.037425\n",
"SVM (scaled) MBEP: -0.038448\n",
"SVM (scaled) R2: 0.828006\n",
"Crossvalidation time: 24.270s\n",
"\n",
"SVM C = 20.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 200.000000\n",
"SVM (scaled) MSE: 10888.380165\n",
"SVM (scaled) RMSE: 104.347401\n",
"SVM (scaled) MAE: 63.787025\n",
"SVM (scaled) MAEP: 0.080645\n",
"SVM (scaled) MBE: -12.434495\n",
"SVM (scaled) MBEP: -0.038844\n",
"SVM (scaled) R2: 0.828328\n",
"Crossvalidation time: 29.165s\n",
"\n",
"SVM C = 20.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 500.000000\n",
"SVM (scaled) MSE: 11158.060278\n",
"SVM (scaled) RMSE: 105.631720\n",
"SVM (scaled) MAE: 64.403732\n",
"SVM (scaled) MAEP: 0.081389\n",
"SVM (scaled) MBE: -12.060488\n",
"SVM (scaled) MBEP: -0.038552\n",
"SVM (scaled) R2: 0.824076\n",
"Crossvalidation time: 40.546s\n",
"\n",
"SVM C = 20.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 700.000000\n",
"SVM (scaled) MSE: 11497.285254\n",
"SVM (scaled) RMSE: 107.225395\n",
"SVM (scaled) MAE: 65.276674\n",
"SVM (scaled) MAEP: 0.082410\n",
"SVM (scaled) MBE: -11.727694\n",
"SVM (scaled) MBEP: -0.038319\n",
"SVM (scaled) R2: 0.818728\n",
"Crossvalidation time: 46.880s\n",
"\n",
"SVM C = 20.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 1000.000000\n",
"SVM (scaled) MSE: 12041.443113\n",
"SVM (scaled) RMSE: 109.733510\n",
"SVM (scaled) MAE: 66.721403\n",
"SVM (scaled) MAEP: 0.083979\n",
"SVM (scaled) MBE: -11.108474\n",
"SVM (scaled) MBEP: -0.037808\n",
"SVM (scaled) R2: 0.810148\n",
"Crossvalidation time: 57.166s\n",
"\n",
"SVM C = 50.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 10.000000\n",
"SVM (scaled) MSE: 12251.971792\n",
"SVM (scaled) RMSE: 110.688625\n",
"SVM (scaled) MAE: 70.204238\n",
"SVM (scaled) MAEP: 0.087902\n",
"SVM (scaled) MBE: -10.151526\n",
"SVM (scaled) MBEP: -0.038844\n",
"SVM (scaled) R2: 0.806829\n",
"Crossvalidation time: 21.472s\n",
"\n",
"SVM C = 50.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 20.000000\n",
"SVM (scaled) MSE: 11549.455450\n",
"SVM (scaled) RMSE: 107.468393\n",
"SVM (scaled) MAE: 66.610685\n",
"SVM (scaled) MAEP: 0.084034\n",
"SVM (scaled) MBE: -11.031012\n",
"SVM (scaled) MBEP: -0.038309\n",
"SVM (scaled) R2: 0.817905\n",
"Crossvalidation time: 23.061s\n",
"\n",
"SVM C = 50.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 50.000000\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"SVM (scaled) MSE: 11072.473790\n",
"SVM (scaled) RMSE: 105.225823\n",
"SVM (scaled) MAE: 64.577883\n",
"SVM (scaled) MAEP: 0.081607\n",
"SVM (scaled) MBE: -12.073269\n",
"SVM (scaled) MBEP: -0.038799\n",
"SVM (scaled) R2: 0.825426\n",
"Crossvalidation time: 26.658s\n",
"\n",
"SVM C = 50.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 70.000000\n",
"SVM (scaled) MSE: 10957.190941\n",
"SVM (scaled) RMSE: 104.676602\n",
"SVM (scaled) MAE: 64.194169\n",
"SVM (scaled) MAEP: 0.081061\n",
"SVM (scaled) MBE: -11.952395\n",
"SVM (scaled) MBEP: -0.038376\n",
"SVM (scaled) R2: 0.827243\n",
"Crossvalidation time: 31.174s\n",
"\n",
"SVM C = 50.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 100.000000\n",
"SVM (scaled) MSE: 10873.860407\n",
"SVM (scaled) RMSE: 104.277804\n",
"SVM (scaled) MAE: 63.886807\n",
"SVM (scaled) MAEP: 0.080758\n",
"SVM (scaled) MBE: -12.265702\n",
"SVM (scaled) MBEP: -0.038585\n",
"SVM (scaled) R2: 0.828557\n",
"Crossvalidation time: 34.215s\n",
"\n",
"SVM C = 50.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 200.000000\n",
"SVM (scaled) MSE: 10964.361763\n",
"SVM (scaled) RMSE: 104.710848\n",
"SVM (scaled) MAE: 64.014650\n",
"SVM (scaled) MAEP: 0.080886\n",
"SVM (scaled) MBE: -12.420074\n",
"SVM (scaled) MBEP: -0.038763\n",
"SVM (scaled) R2: 0.827130\n",
"Crossvalidation time: 43.657s\n",
"\n",
"SVM C = 50.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 500.000000\n",
"SVM (scaled) MSE: 11446.444435\n",
"SVM (scaled) RMSE: 106.988057\n",
"SVM (scaled) MAE: 65.126017\n",
"SVM (scaled) MAEP: 0.082268\n",
"SVM (scaled) MBE: -11.749879\n",
"SVM (scaled) MBEP: -0.038284\n",
"SVM (scaled) R2: 0.819529\n",
"Crossvalidation time: 71.328s\n",
"\n",
"SVM C = 50.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 700.000000\n",
"SVM (scaled) MSE: 11985.079385\n",
"SVM (scaled) RMSE: 109.476387\n",
"SVM (scaled) MAE: 66.445264\n",
"SVM (scaled) MAEP: 0.083813\n",
"SVM (scaled) MBE: -11.330979\n",
"SVM (scaled) MBEP: -0.038016\n",
"SVM (scaled) R2: 0.811037\n",
"Crossvalidation time: 91.222s\n",
"\n",
"SVM C = 50.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 1000.000000\n",
"SVM (scaled) MSE: 12866.107143\n",
"SVM (scaled) RMSE: 113.428864\n",
"SVM (scaled) MAE: 68.295084\n",
"SVM (scaled) MAEP: 0.085836\n",
"SVM (scaled) MBE: -10.607349\n",
"SVM (scaled) MBEP: -0.037510\n",
"SVM (scaled) R2: 0.797146\n",
"Crossvalidation time: 117.494s\n",
"\n",
"SVM C = 70.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 10.000000\n",
"SVM (scaled) MSE: 12160.727321\n",
"SVM (scaled) RMSE: 110.275688\n",
"SVM (scaled) MAE: 69.625884\n",
"SVM (scaled) MAEP: 0.087366\n",
"SVM (scaled) MBE: -10.264861\n",
"SVM (scaled) MBEP: -0.038562\n",
"SVM (scaled) R2: 0.808268\n",
"Crossvalidation time: 24.074s\n",
"\n",
"SVM C = 70.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 20.000000\n",
"SVM (scaled) MSE: 11468.539386\n",
"SVM (scaled) RMSE: 107.091267\n",
"SVM (scaled) MAE: 66.233570\n",
"SVM (scaled) MAEP: 0.083585\n",
"SVM (scaled) MBE: -11.107879\n",
"SVM (scaled) MBEP: -0.038277\n",
"SVM (scaled) R2: 0.819181\n",
"Crossvalidation time: 25.995s\n",
"\n",
"SVM C = 70.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 50.000000\n",
"SVM (scaled) MSE: 11053.431671\n",
"SVM (scaled) RMSE: 105.135302\n",
"SVM (scaled) MAE: 64.525303\n",
"SVM (scaled) MAEP: 0.081530\n",
"SVM (scaled) MBE: -12.013512\n",
"SVM (scaled) MBEP: -0.038628\n",
"SVM (scaled) R2: 0.825726\n",
"Crossvalidation time: 31.432s\n",
"\n",
"SVM C = 70.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 70.000000\n",
"SVM (scaled) MSE: 10931.674136\n",
"SVM (scaled) RMSE: 104.554647\n",
"SVM (scaled) MAE: 64.085255\n",
"SVM (scaled) MAEP: 0.080940\n",
"SVM (scaled) MBE: -11.974629\n",
"SVM (scaled) MBEP: -0.038337\n",
"SVM (scaled) R2: 0.827645\n",
"Crossvalidation time: 35.203s\n",
"\n",
"SVM C = 70.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 100.000000\n",
"SVM (scaled) MSE: 10861.833773\n",
"SVM (scaled) RMSE: 104.220122\n",
"SVM (scaled) MAE: 63.840338\n",
"SVM (scaled) MAEP: 0.080723\n",
"SVM (scaled) MBE: -12.309372\n",
"SVM (scaled) MBEP: -0.038604\n",
"SVM (scaled) R2: 0.828747\n",
"Crossvalidation time: 40.663s\n",
"\n",
"SVM C = 70.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 200.000000\n",
"SVM (scaled) MSE: 10986.631860\n",
"SVM (scaled) RMSE: 104.817135\n",
"SVM (scaled) MAE: 64.085593\n",
"SVM (scaled) MAEP: 0.080942\n",
"SVM (scaled) MBE: -12.357526\n",
"SVM (scaled) MBEP: -0.038685\n",
"SVM (scaled) R2: 0.826779\n",
"Crossvalidation time: 54.250s\n",
"\n",
"SVM C = 70.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 500.000000\n",
"SVM (scaled) MSE: 11582.323742\n",
"SVM (scaled) RMSE: 107.621205\n",
"SVM (scaled) MAE: 65.446652\n",
"SVM (scaled) MAEP: 0.082679\n",
"SVM (scaled) MBE: -11.629362\n",
"SVM (scaled) MBEP: -0.038228\n",
"SVM (scaled) R2: 0.817387\n",
"Crossvalidation time: 89.637s\n",
"\n",
"SVM C = 70.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 700.000000\n",
"SVM (scaled) MSE: 12204.182541\n",
"SVM (scaled) RMSE: 110.472542\n",
"SVM (scaled) MAE: 66.920293\n",
"SVM (scaled) MAEP: 0.084390\n",
"SVM (scaled) MBE: -11.241513\n",
"SVM (scaled) MBEP: -0.037934\n",
"SVM (scaled) R2: 0.807582\n",
"Crossvalidation time: 115.106s\n",
"\n",
"SVM C = 70.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 1000.000000\n",
"SVM (scaled) MSE: 13317.619253\n",
"SVM (scaled) RMSE: 115.401990\n",
"SVM (scaled) MAE: 69.094689\n",
"SVM (scaled) MAEP: 0.086857\n",
"SVM (scaled) MBE: -10.302672\n",
"SVM (scaled) MBEP: -0.037346\n",
"SVM (scaled) R2: 0.790027\n",
"Crossvalidation time: 156.138s\n",
"\n",
"SVM C = 100.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 10.000000\n",
"SVM (scaled) MSE: 12084.074446\n",
"SVM (scaled) RMSE: 109.927587\n",
"SVM (scaled) MAE: 69.117692\n",
"SVM (scaled) MAEP: 0.086875\n",
"SVM (scaled) MBE: -10.537588\n",
"SVM (scaled) MBEP: -0.038451\n",
"SVM (scaled) R2: 0.809476\n",
"Crossvalidation time: 24.476s\n",
"\n",
"SVM C = 100.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 20.000000\n",
"SVM (scaled) MSE: 11392.699653\n",
"SVM (scaled) RMSE: 106.736590\n",
"SVM (scaled) MAE: 65.897491\n",
"SVM (scaled) MAEP: 0.083195\n",
"SVM (scaled) MBE: -11.207495\n",
"SVM (scaled) MBEP: -0.038285\n",
"SVM (scaled) R2: 0.820377\n",
"Crossvalidation time: 28.507s\n",
"\n",
"SVM C = 100.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 50.000000\n",
"SVM (scaled) MSE: 11025.459373\n",
"SVM (scaled) RMSE: 105.002187\n",
"SVM (scaled) MAE: 64.427025\n",
"SVM (scaled) MAEP: 0.081386\n",
"SVM (scaled) MBE: -12.015131\n",
"SVM (scaled) MBEP: -0.038515\n",
"SVM (scaled) R2: 0.826167\n",
"Crossvalidation time: 35.904s\n",
"\n",
"SVM C = 100.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 70.000000\n",
"SVM (scaled) MSE: 10912.187161\n",
"SVM (scaled) RMSE: 104.461415\n",
"SVM (scaled) MAE: 64.001477\n",
"SVM (scaled) MAEP: 0.080868\n",
"SVM (scaled) MBE: -12.029018\n",
"SVM (scaled) MBEP: -0.038345\n",
"SVM (scaled) R2: 0.827953\n",
"Crossvalidation time: 40.844s\n",
"\n",
"SVM C = 100.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 100.000000\n",
"SVM (scaled) MSE: 10854.523489\n",
"SVM (scaled) RMSE: 104.185044\n",
"SVM (scaled) MAE: 63.838218\n",
"SVM (scaled) MAEP: 0.080730\n",
"SVM (scaled) MBE: -12.348117\n",
"SVM (scaled) MBEP: -0.038628\n",
"SVM (scaled) R2: 0.828862\n",
"Crossvalidation time: 48.681s\n",
"\n",
"SVM C = 100.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 200.000000\n",
"SVM (scaled) MSE: 11014.925217\n",
"SVM (scaled) RMSE: 104.952014\n",
"SVM (scaled) MAE: 64.177831\n",
"SVM (scaled) MAEP: 0.081015\n",
"SVM (scaled) MBE: -12.299085\n",
"SVM (scaled) MBEP: -0.038619\n",
"SVM (scaled) R2: 0.826333\n",
"Crossvalidation time: 69.666s\n",
"\n",
"SVM C = 100.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 500.000000\n",
"SVM (scaled) MSE: 11764.177295\n",
"SVM (scaled) RMSE: 108.462792\n",
"SVM (scaled) MAE: 65.851721\n",
"SVM (scaled) MAEP: 0.083256\n",
"SVM (scaled) MBE: -11.554232\n",
"SVM (scaled) MBEP: -0.038241\n",
"SVM (scaled) R2: 0.814520\n",
"Crossvalidation time: 130.173s\n",
"\n",
"SVM C = 100.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 700.000000\n",
"SVM (scaled) MSE: 12518.522405\n",
"SVM (scaled) RMSE: 111.886203\n",
"SVM (scaled) MAE: 67.472509\n",
"SVM (scaled) MAEP: 0.085078\n",
"SVM (scaled) MBE: -11.113887\n",
"SVM (scaled) MBEP: -0.037828\n",
"SVM (scaled) R2: 0.802626\n",
"Crossvalidation time: 159.096s\n",
"\n",
"SVM C = 100.000000\n",
"SVM epsilon = 0.010000\n",
"SVM gamma = 1000.000000\n",
"SVM (scaled) MSE: 13849.084554\n",
"SVM (scaled) RMSE: 117.682134\n",
"SVM (scaled) MAE: 69.977586\n",
"SVM (scaled) MAEP: 0.088176\n",
"SVM (scaled) MBE: -10.171039\n",
"SVM (scaled) MBEP: -0.037506\n",
"SVM (scaled) R2: 0.781648\n",
"Crossvalidation time: 231.256s\n",
"\n",
"SVM C = 1.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 10.000000\n",
"SVM (scaled) MSE: 20307.024475\n",
"SVM (scaled) RMSE: 142.502717\n",
"SVM (scaled) MAE: 100.961641\n",
"SVM (scaled) MAEP: 0.121548\n",
"SVM (scaled) MBE: -13.753743\n",
"SVM (scaled) MBEP: -0.051787\n",
"SVM (scaled) R2: 0.679829\n",
"Crossvalidation time: 15.881s\n",
"\n",
"SVM C = 1.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 20.000000\n",
"SVM (scaled) MSE: 15249.697176\n",
"SVM (scaled) RMSE: 123.489664\n",
"SVM (scaled) MAE: 83.875305\n",
"SVM (scaled) MAEP: 0.102811\n",
"SVM (scaled) MBE: -11.161683\n",
"SVM (scaled) MBEP: -0.045482\n",
"SVM (scaled) R2: 0.759565\n",
"Crossvalidation time: 15.070s\n",
"\n",
"SVM C = 1.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 50.000000\n",
"SVM (scaled) MSE: 11742.052590\n",
"SVM (scaled) RMSE: 108.360752\n",
"SVM (scaled) MAE: 67.649580\n",
"SVM (scaled) MAEP: 0.085307\n",
"SVM (scaled) MBE: -10.884603\n",
"SVM (scaled) MBEP: -0.040064\n",
"SVM (scaled) R2: 0.814869\n",
"Crossvalidation time: 13.338s\n",
"\n",
"SVM C = 1.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 70.000000\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"SVM (scaled) MSE: 11392.317593\n",
"SVM (scaled) RMSE: 106.734800\n",
"SVM (scaled) MAE: 65.960555\n",
"SVM (scaled) MAEP: 0.083441\n",
"SVM (scaled) MBE: -11.023122\n",
"SVM (scaled) MBEP: -0.039429\n",
"SVM (scaled) R2: 0.820383\n",
"Crossvalidation time: 13.075s\n",
"\n",
"SVM C = 1.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 100.000000\n",
"SVM (scaled) MSE: 11167.089480\n",
"SVM (scaled) RMSE: 105.674450\n",
"SVM (scaled) MAE: 65.076427\n",
"SVM (scaled) MAEP: 0.082439\n",
"SVM (scaled) MBE: -10.864001\n",
"SVM (scaled) MBEP: -0.038811\n",
"SVM (scaled) R2: 0.823934\n",
"Crossvalidation time: 13.103s\n",
"\n",
"SVM C = 1.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 200.000000\n",
"SVM (scaled) MSE: 10895.791177\n",
"SVM (scaled) RMSE: 104.382907\n",
"SVM (scaled) MAE: 64.229407\n",
"SVM (scaled) MAEP: 0.081368\n",
"SVM (scaled) MBE: -10.577629\n",
"SVM (scaled) MBEP: -0.038022\n",
"SVM (scaled) R2: 0.828211\n",
"Crossvalidation time: 13.561s\n",
"\n",
"SVM C = 1.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 500.000000\n",
"SVM (scaled) MSE: 10785.526112\n",
"SVM (scaled) RMSE: 103.853388\n",
"SVM (scaled) MAE: 63.778495\n",
"SVM (scaled) MAEP: 0.080803\n",
"SVM (scaled) MBE: -10.159342\n",
"SVM (scaled) MBEP: -0.037292\n",
"SVM (scaled) R2: 0.829950\n",
"Crossvalidation time: 13.907s\n",
"\n",
"SVM C = 1.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 700.000000\n",
"SVM (scaled) MSE: 10837.105742\n",
"SVM (scaled) RMSE: 104.101420\n",
"SVM (scaled) MAE: 63.862487\n",
"SVM (scaled) MAEP: 0.080983\n",
"SVM (scaled) MBE: -10.068455\n",
"SVM (scaled) MBEP: -0.037273\n",
"SVM (scaled) R2: 0.829136\n",
"Crossvalidation time: 14.675s\n",
"\n",
"SVM C = 1.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 1000.000000\n",
"SVM (scaled) MSE: 10913.378448\n",
"SVM (scaled) RMSE: 104.467117\n",
"SVM (scaled) MAE: 63.955915\n",
"SVM (scaled) MAEP: 0.081242\n",
"SVM (scaled) MBE: -10.017158\n",
"SVM (scaled) MBEP: -0.037402\n",
"SVM (scaled) R2: 0.827934\n",
"Crossvalidation time: 14.865s\n",
"\n",
"SVM C = 2.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 10.000000\n",
"SVM (scaled) MSE: 18817.899665\n",
"SVM (scaled) RMSE: 137.178350\n",
"SVM (scaled) MAE: 96.554790\n",
"SVM (scaled) MAEP: 0.116435\n",
"SVM (scaled) MBE: -12.515686\n",
"SVM (scaled) MBEP: -0.049219\n",
"SVM (scaled) R2: 0.703307\n",
"Crossvalidation time: 16.645s\n",
"\n",
"SVM C = 2.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 20.000000\n",
"SVM (scaled) MSE: 13479.311195\n",
"SVM (scaled) RMSE: 116.100436\n",
"SVM (scaled) MAE: 76.363951\n",
"SVM (scaled) MAEP: 0.094553\n",
"SVM (scaled) MBE: -10.601742\n",
"SVM (scaled) MBEP: -0.042722\n",
"SVM (scaled) R2: 0.787478\n",
"Crossvalidation time: 15.034s\n",
"\n",
"SVM C = 2.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 50.000000\n",
"SVM (scaled) MSE: 11469.134439\n",
"SVM (scaled) RMSE: 107.094045\n",
"SVM (scaled) MAE: 66.347006\n",
"SVM (scaled) MAEP: 0.083805\n",
"SVM (scaled) MBE: -10.917051\n",
"SVM (scaled) MBEP: -0.039176\n",
"SVM (scaled) R2: 0.819172\n",
"Crossvalidation time: 13.836s\n",
"\n",
"SVM C = 2.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 70.000000\n",
"SVM (scaled) MSE: 11227.511855\n",
"SVM (scaled) RMSE: 105.959954\n",
"SVM (scaled) MAE: 65.283961\n",
"SVM (scaled) MAEP: 0.082599\n",
"SVM (scaled) MBE: -10.928408\n",
"SVM (scaled) MBEP: -0.038713\n",
"SVM (scaled) R2: 0.822981\n",
"Crossvalidation time: 14.242s\n",
"\n",
"SVM C = 2.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 100.000000\n",
"SVM (scaled) MSE: 11040.780735\n",
"SVM (scaled) RMSE: 105.075119\n",
"SVM (scaled) MAE: 64.669223\n",
"SVM (scaled) MAEP: 0.081825\n",
"SVM (scaled) MBE: -10.622488\n",
"SVM (scaled) MBEP: -0.038138\n",
"SVM (scaled) R2: 0.825925\n",
"Crossvalidation time: 13.922s\n",
"\n",
"SVM C = 2.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 200.000000\n",
"SVM (scaled) MSE: 10828.114008\n",
"SVM (scaled) RMSE: 104.058224\n",
"SVM (scaled) MAE: 63.966787\n",
"SVM (scaled) MAEP: 0.080962\n",
"SVM (scaled) MBE: -10.518345\n",
"SVM (scaled) MBEP: -0.037607\n",
"SVM (scaled) R2: 0.829278\n",
"Crossvalidation time: 14.505s\n",
"\n",
"SVM C = 2.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 500.000000\n",
"SVM (scaled) MSE: 10815.396121\n",
"SVM (scaled) RMSE: 103.997097\n",
"SVM (scaled) MAE: 63.828414\n",
"SVM (scaled) MAEP: 0.080742\n",
"SVM (scaled) MBE: -9.879050\n",
"SVM (scaled) MBEP: -0.036778\n",
"SVM (scaled) R2: 0.829479\n",
"Crossvalidation time: 15.547s\n",
"\n",
"SVM C = 2.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 700.000000\n",
"SVM (scaled) MSE: 10866.260550\n",
"SVM (scaled) RMSE: 104.241357\n",
"SVM (scaled) MAE: 63.860965\n",
"SVM (scaled) MAEP: 0.080830\n",
"SVM (scaled) MBE: -9.812377\n",
"SVM (scaled) MBEP: -0.036720\n",
"SVM (scaled) R2: 0.828677\n",
"Crossvalidation time: 16.125s\n",
"\n",
"SVM C = 2.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 1000.000000\n",
"SVM (scaled) MSE: 10966.374548\n",
"SVM (scaled) RMSE: 104.720459\n",
"SVM (scaled) MAE: 64.043708\n",
"SVM (scaled) MAEP: 0.081079\n",
"SVM (scaled) MBE: -9.748340\n",
"SVM (scaled) MBEP: -0.036685\n",
"SVM (scaled) R2: 0.827098\n",
"Crossvalidation time: 16.855s\n",
"\n",
"SVM C = 5.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 10.000000\n",
"SVM (scaled) MSE: 16045.085837\n",
"SVM (scaled) RMSE: 126.669198\n",
"SVM (scaled) MAE: 87.393629\n",
"SVM (scaled) MAEP: 0.106196\n",
"SVM (scaled) MBE: -10.541027\n",
"SVM (scaled) MBEP: -0.045097\n",
"SVM (scaled) R2: 0.747025\n",
"Crossvalidation time: 16.550s\n",
"\n",
"SVM C = 5.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 20.000000\n",
"SVM (scaled) MSE: 12375.840897\n",
"SVM (scaled) RMSE: 111.246757\n",
"SVM (scaled) MAE: 71.000365\n",
"SVM (scaled) MAEP: 0.088688\n",
"SVM (scaled) MBE: -10.692465\n",
"SVM (scaled) MBEP: -0.040472\n",
"SVM (scaled) R2: 0.804876\n",
"Crossvalidation time: 14.696s\n",
"\n",
"SVM C = 5.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 50.000000\n",
"SVM (scaled) MSE: 11250.573126\n",
"SVM (scaled) RMSE: 106.068719\n",
"SVM (scaled) MAE: 65.409722\n",
"SVM (scaled) MAEP: 0.082689\n",
"SVM (scaled) MBE: -10.733181\n",
"SVM (scaled) MBEP: -0.038280\n",
"SVM (scaled) R2: 0.822618\n",
"Crossvalidation time: 14.660s\n",
"\n",
"SVM C = 5.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 70.000000\n",
"SVM (scaled) MSE: 11065.885956\n",
"SVM (scaled) RMSE: 105.194515\n",
"SVM (scaled) MAE: 64.738954\n",
"SVM (scaled) MAEP: 0.081815\n",
"SVM (scaled) MBE: -10.487173\n",
"SVM (scaled) MBEP: -0.037787\n",
"SVM (scaled) R2: 0.825529\n",
"Crossvalidation time: 14.456s\n",
"\n",
"SVM C = 5.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 100.000000\n",
"SVM (scaled) MSE: 10935.759702\n",
"SVM (scaled) RMSE: 104.574183\n",
"SVM (scaled) MAE: 64.347953\n",
"SVM (scaled) MAEP: 0.081322\n",
"SVM (scaled) MBE: -10.494350\n",
"SVM (scaled) MBEP: -0.037558\n",
"SVM (scaled) R2: 0.827581\n",
"Crossvalidation time: 14.642s\n",
"\n",
"SVM C = 5.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 200.000000\n",
"SVM (scaled) MSE: 10778.246657\n",
"SVM (scaled) RMSE: 103.818335\n",
"SVM (scaled) MAE: 63.778495\n",
"SVM (scaled) MAEP: 0.080576\n",
"SVM (scaled) MBE: -10.173227\n",
"SVM (scaled) MBEP: -0.036924\n",
"SVM (scaled) R2: 0.830064\n",
"Crossvalidation time: 16.023s\n",
"\n",
"SVM C = 5.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 500.000000\n",
"SVM (scaled) MSE: 10863.659996\n",
"SVM (scaled) RMSE: 104.228883\n",
"SVM (scaled) MAE: 63.890321\n",
"SVM (scaled) MAEP: 0.080698\n",
"SVM (scaled) MBE: -9.823261\n",
"SVM (scaled) MBEP: -0.036470\n",
"SVM (scaled) R2: 0.828718\n",
"Crossvalidation time: 18.051s\n",
"\n",
"SVM C = 5.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 700.000000\n",
"SVM (scaled) MSE: 10967.483566\n",
"SVM (scaled) RMSE: 104.725754\n",
"SVM (scaled) MAE: 64.017529\n",
"SVM (scaled) MAEP: 0.080987\n",
"SVM (scaled) MBE: -9.655350\n",
"SVM (scaled) MBEP: -0.036472\n",
"SVM (scaled) R2: 0.827081\n",
"Crossvalidation time: 19.129s\n",
"\n",
"SVM C = 5.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 1000.000000\n",
"SVM (scaled) MSE: 11182.165096\n",
"SVM (scaled) RMSE: 105.745757\n",
"SVM (scaled) MAE: 64.582578\n",
"SVM (scaled) MAEP: 0.081634\n",
"SVM (scaled) MBE: -9.535967\n",
"SVM (scaled) MBEP: -0.036377\n",
"SVM (scaled) R2: 0.823696\n",
"Crossvalidation time: 20.867s\n",
"\n",
"SVM C = 7.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 10.000000\n",
"SVM (scaled) MSE: 15032.975530\n",
"SVM (scaled) RMSE: 122.609035\n",
"SVM (scaled) MAE: 83.574435\n",
"SVM (scaled) MAEP: 0.102033\n",
"SVM (scaled) MBE: -9.878080\n",
"SVM (scaled) MBEP: -0.043600\n",
"SVM (scaled) R2: 0.762982\n",
"Crossvalidation time: 16.195s\n",
"\n",
"SVM C = 7.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 20.000000\n",
"SVM (scaled) MSE: 12162.638287\n",
"SVM (scaled) RMSE: 110.284352\n",
"SVM (scaled) MAE: 69.873365\n",
"SVM (scaled) MAEP: 0.087532\n",
"SVM (scaled) MBE: -10.557505\n",
"SVM (scaled) MBEP: -0.039701\n",
"SVM (scaled) R2: 0.808237\n",
"Crossvalidation time: 13.767s\n",
"\n",
"SVM C = 7.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 50.000000\n",
"SVM (scaled) MSE: 11179.165959\n",
"SVM (scaled) RMSE: 105.731575\n",
"SVM (scaled) MAE: 65.158755\n",
"SVM (scaled) MAEP: 0.082330\n",
"SVM (scaled) MBE: -10.549755\n",
"SVM (scaled) MBEP: -0.037898\n",
"SVM (scaled) R2: 0.823743\n",
"Crossvalidation time: 13.605s\n",
"\n",
"SVM C = 7.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 70.000000\n",
"SVM (scaled) MSE: 11032.806020\n",
"SVM (scaled) RMSE: 105.037165\n",
"SVM (scaled) MAE: 64.628273\n",
"SVM (scaled) MAEP: 0.081694\n",
"SVM (scaled) MBE: -10.542628\n",
"SVM (scaled) MBEP: -0.037747\n",
"SVM (scaled) R2: 0.826051\n",
"Crossvalidation time: 14.143s\n",
"\n",
"SVM C = 7.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 100.000000\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"SVM (scaled) MSE: 10902.844117\n",
"SVM (scaled) RMSE: 104.416685\n",
"SVM (scaled) MAE: 64.237152\n",
"SVM (scaled) MAEP: 0.081148\n",
"SVM (scaled) MBE: -10.388831\n",
"SVM (scaled) MBEP: -0.037335\n",
"SVM (scaled) R2: 0.828100\n",
"Crossvalidation time: 14.373s\n",
"\n",
"SVM C = 7.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 200.000000\n",
"SVM (scaled) MSE: 10772.236024\n",
"SVM (scaled) RMSE: 103.789383\n",
"SVM (scaled) MAE: 63.761719\n",
"SVM (scaled) MAEP: 0.080502\n",
"SVM (scaled) MBE: -10.063661\n",
"SVM (scaled) MBEP: -0.036708\n",
"SVM (scaled) R2: 0.830159\n",
"Crossvalidation time: 16.537s\n",
"\n",
"SVM C = 7.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 500.000000\n",
"SVM (scaled) MSE: 10890.239851\n",
"SVM (scaled) RMSE: 104.356312\n",
"SVM (scaled) MAE: 63.946738\n",
"SVM (scaled) MAEP: 0.080768\n",
"SVM (scaled) MBE: -9.782195\n",
"SVM (scaled) MBEP: -0.036429\n",
"SVM (scaled) R2: 0.828299\n",
"Crossvalidation time: 21.054s\n",
"\n",
"SVM C = 7.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 700.000000\n",
"SVM (scaled) MSE: 11023.854386\n",
"SVM (scaled) RMSE: 104.994545\n",
"SVM (scaled) MAE: 64.163619\n",
"SVM (scaled) MAEP: 0.081146\n",
"SVM (scaled) MBE: -9.571552\n",
"SVM (scaled) MBEP: -0.036367\n",
"SVM (scaled) R2: 0.826192\n",
"Crossvalidation time: 22.652s\n",
"\n",
"SVM C = 7.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 1000.000000\n",
"SVM (scaled) MSE: 11317.651213\n",
"SVM (scaled) RMSE: 106.384450\n",
"SVM (scaled) MAE: 64.948549\n",
"SVM (scaled) MAEP: 0.082049\n",
"SVM (scaled) MBE: -9.460930\n",
"SVM (scaled) MBEP: -0.036294\n",
"SVM (scaled) R2: 0.821560\n",
"Crossvalidation time: 25.370s\n",
"\n",
"SVM C = 10.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 10.000000\n",
"SVM (scaled) MSE: 14062.035871\n",
"SVM (scaled) RMSE: 118.583455\n",
"SVM (scaled) MAE: 79.611997\n",
"SVM (scaled) MAEP: 0.097658\n",
"SVM (scaled) MBE: -9.398804\n",
"SVM (scaled) MBEP: -0.041922\n",
"SVM (scaled) R2: 0.778291\n",
"Crossvalidation time: 17.411s\n",
"\n",
"SVM C = 10.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 20.000000\n",
"SVM (scaled) MSE: 11987.717539\n",
"SVM (scaled) RMSE: 109.488436\n",
"SVM (scaled) MAE: 68.969488\n",
"SVM (scaled) MAEP: 0.086555\n",
"SVM (scaled) MBE: -10.405289\n",
"SVM (scaled) MBEP: -0.039019\n",
"SVM (scaled) R2: 0.810995\n",
"Crossvalidation time: 16.384s\n",
"\n",
"SVM C = 10.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 50.000000\n",
"SVM (scaled) MSE: 11123.518503\n",
"SVM (scaled) RMSE: 105.468092\n",
"SVM (scaled) MAE: 64.948391\n",
"SVM (scaled) MAEP: 0.082044\n",
"SVM (scaled) MBE: -10.444227\n",
"SVM (scaled) MBEP: -0.037647\n",
"SVM (scaled) R2: 0.824621\n",
"Crossvalidation time: 16.153s\n",
"\n",
"SVM C = 10.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 70.000000\n",
"SVM (scaled) MSE: 10992.280211\n",
"SVM (scaled) RMSE: 104.844076\n",
"SVM (scaled) MAE: 64.513112\n",
"SVM (scaled) MAEP: 0.081512\n",
"SVM (scaled) MBE: -10.476422\n",
"SVM (scaled) MBEP: -0.037552\n",
"SVM (scaled) R2: 0.826690\n",
"Crossvalidation time: 16.195s\n",
"\n",
"SVM C = 10.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 100.000000\n",
"SVM (scaled) MSE: 10874.261446\n",
"SVM (scaled) RMSE: 104.279727\n",
"SVM (scaled) MAE: 64.132115\n",
"SVM (scaled) MAEP: 0.081006\n",
"SVM (scaled) MBE: -10.306060\n",
"SVM (scaled) MBEP: -0.037159\n",
"SVM (scaled) R2: 0.828551\n",
"Crossvalidation time: 16.883s\n",
"\n",
"SVM C = 10.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 200.000000\n",
"SVM (scaled) MSE: 10771.550398\n",
"SVM (scaled) RMSE: 103.786080\n",
"SVM (scaled) MAE: 63.749605\n",
"SVM (scaled) MAEP: 0.080474\n",
"SVM (scaled) MBE: -9.961846\n",
"SVM (scaled) MBEP: -0.036546\n",
"SVM (scaled) R2: 0.830170\n",
"Crossvalidation time: 19.201s\n",
"\n",
"SVM C = 10.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 500.000000\n",
"SVM (scaled) MSE: 10924.557941\n",
"SVM (scaled) RMSE: 104.520610\n",
"SVM (scaled) MAE: 64.035449\n",
"SVM (scaled) MAEP: 0.080877\n",
"SVM (scaled) MBE: -9.733897\n",
"SVM (scaled) MBEP: -0.036397\n",
"SVM (scaled) R2: 0.827758\n",
"Crossvalidation time: 23.582s\n",
"\n",
"SVM C = 10.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 700.000000\n",
"SVM (scaled) MSE: 11102.244251\n",
"SVM (scaled) RMSE: 105.367188\n",
"SVM (scaled) MAE: 64.342529\n",
"SVM (scaled) MAEP: 0.081310\n",
"SVM (scaled) MBE: -9.434421\n",
"SVM (scaled) MBEP: -0.036184\n",
"SVM (scaled) R2: 0.824956\n",
"Crossvalidation time: 25.100s\n",
"\n",
"SVM C = 10.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 1000.000000\n",
"SVM (scaled) MSE: 11483.042338\n",
"SVM (scaled) RMSE: 107.158958\n",
"SVM (scaled) MAE: 65.349748\n",
"SVM (scaled) MAEP: 0.082520\n",
"SVM (scaled) MBE: -9.380459\n",
"SVM (scaled) MBEP: -0.036221\n",
"SVM (scaled) R2: 0.818952\n",
"Crossvalidation time: 27.531s\n",
"\n",
"SVM C = 20.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 10.000000\n",
"SVM (scaled) MSE: 12916.214920\n",
"SVM (scaled) RMSE: 113.649527\n",
"SVM (scaled) MAE: 74.090716\n",
"SVM (scaled) MAEP: 0.091768\n",
"SVM (scaled) MBE: -9.891285\n",
"SVM (scaled) MBEP: -0.040448\n",
"SVM (scaled) R2: 0.796356\n",
"Crossvalidation time: 15.942s\n",
"\n",
"SVM C = 20.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 20.000000\n",
"SVM (scaled) MSE: 11766.479304\n",
"SVM (scaled) RMSE: 108.473404\n",
"SVM (scaled) MAE: 67.817723\n",
"SVM (scaled) MAEP: 0.085364\n",
"SVM (scaled) MBE: -10.382298\n",
"SVM (scaled) MBEP: -0.038269\n",
"SVM (scaled) R2: 0.814484\n",
"Crossvalidation time: 15.359s\n",
"\n",
"SVM C = 20.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 50.000000\n",
"SVM (scaled) MSE: 11043.130285\n",
"SVM (scaled) RMSE: 105.086299\n",
"SVM (scaled) MAE: 64.669423\n",
"SVM (scaled) MAEP: 0.081671\n",
"SVM (scaled) MBE: -10.262892\n",
"SVM (scaled) MBEP: -0.037300\n",
"SVM (scaled) R2: 0.825888\n",
"Crossvalidation time: 16.325s\n",
"\n",
"SVM C = 20.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 70.000000\n",
"SVM (scaled) MSE: 10934.406553\n",
"SVM (scaled) RMSE: 104.567713\n",
"SVM (scaled) MAE: 64.328127\n",
"SVM (scaled) MAEP: 0.081216\n",
"SVM (scaled) MBE: -10.318254\n",
"SVM (scaled) MBEP: -0.037177\n",
"SVM (scaled) R2: 0.827602\n",
"Crossvalidation time: 17.317s\n",
"\n",
"SVM C = 20.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 100.000000\n",
"SVM (scaled) MSE: 10825.896525\n",
"SVM (scaled) RMSE: 104.047569\n",
"SVM (scaled) MAE: 63.951895\n",
"SVM (scaled) MAEP: 0.080726\n",
"SVM (scaled) MBE: -10.156733\n",
"SVM (scaled) MBEP: -0.036858\n",
"SVM (scaled) R2: 0.829313\n",
"Crossvalidation time: 18.941s\n",
"\n",
"SVM C = 20.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 200.000000\n",
"SVM (scaled) MSE: 10796.100525\n",
"SVM (scaled) RMSE: 103.904285\n",
"SVM (scaled) MAE: 63.799617\n",
"SVM (scaled) MAEP: 0.080540\n",
"SVM (scaled) MBE: -9.904966\n",
"SVM (scaled) MBEP: -0.036370\n",
"SVM (scaled) R2: 0.829783\n",
"Crossvalidation time: 21.776s\n",
"\n",
"SVM C = 20.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 500.000000\n",
"SVM (scaled) MSE: 11062.876877\n",
"SVM (scaled) RMSE: 105.180211\n",
"SVM (scaled) MAE: 64.290901\n",
"SVM (scaled) MAEP: 0.081240\n",
"SVM (scaled) MBE: -9.540432\n",
"SVM (scaled) MBEP: -0.036271\n",
"SVM (scaled) R2: 0.825577\n",
"Crossvalidation time: 29.446s\n",
"\n",
"SVM C = 20.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 700.000000\n",
"SVM (scaled) MSE: 11340.957519\n",
"SVM (scaled) RMSE: 106.493932\n",
"SVM (scaled) MAE: 64.951063\n",
"SVM (scaled) MAEP: 0.082028\n",
"SVM (scaled) MBE: -9.284026\n",
"SVM (scaled) MBEP: -0.035991\n",
"SVM (scaled) R2: 0.821193\n",
"Crossvalidation time: 34.156s\n",
"\n",
"SVM C = 20.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 1000.000000\n",
"SVM (scaled) MSE: 11879.924463\n",
"SVM (scaled) RMSE: 108.995066\n",
"SVM (scaled) MAE: 66.263897\n",
"SVM (scaled) MAEP: 0.083541\n",
"SVM (scaled) MBE: -9.114376\n",
"SVM (scaled) MBEP: -0.035958\n",
"SVM (scaled) R2: 0.812695\n",
"Crossvalidation time: 43.081s\n",
"\n",
"SVM C = 50.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 10.000000\n",
"SVM (scaled) MSE: 12279.293901\n",
"SVM (scaled) RMSE: 110.811975\n",
"SVM (scaled) MAE: 70.554522\n",
"SVM (scaled) MAEP: 0.088183\n",
"SVM (scaled) MBE: -10.088976\n",
"SVM (scaled) MBEP: -0.038832\n",
"SVM (scaled) R2: 0.806398\n",
"Crossvalidation time: 16.388s\n",
"\n",
"SVM C = 50.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 20.000000\n",
"SVM (scaled) MSE: 11522.922860\n",
"SVM (scaled) RMSE: 107.344878\n",
"SVM (scaled) MAE: 66.681713\n",
"SVM (scaled) MAEP: 0.084077\n",
"SVM (scaled) MBE: -10.264338\n",
"SVM (scaled) MBEP: -0.037592\n",
"SVM (scaled) R2: 0.818324\n",
"Crossvalidation time: 16.726s\n",
"\n",
"SVM C = 50.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 50.000000\n",
"SVM (scaled) MSE: 10974.490755\n",
"SVM (scaled) RMSE: 104.759204\n",
"SVM (scaled) MAE: 64.441366\n",
"SVM (scaled) MAEP: 0.081407\n",
"SVM (scaled) MBE: -10.317032\n",
"SVM (scaled) MBEP: -0.037153\n",
"SVM (scaled) R2: 0.826970\n",
"Crossvalidation time: 19.152s\n",
"\n",
"SVM C = 50.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 70.000000\n",
"SVM (scaled) MSE: 10865.080122\n",
"SVM (scaled) RMSE: 104.235695\n",
"SVM (scaled) MAE: 64.088529\n",
"SVM (scaled) MAEP: 0.080904\n",
"SVM (scaled) MBE: -10.110453\n",
"SVM (scaled) MBEP: -0.036776\n",
"SVM (scaled) R2: 0.828695\n",
"Crossvalidation time: 21.673s\n",
"\n",
"SVM C = 50.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 100.000000\n",
"SVM (scaled) MSE: 10787.007252\n",
"SVM (scaled) RMSE: 103.860518\n",
"SVM (scaled) MAE: 63.841514\n",
"SVM (scaled) MAEP: 0.080554\n",
"SVM (scaled) MBE: -9.890666\n",
"SVM (scaled) MBEP: -0.036436\n",
"SVM (scaled) R2: 0.829926\n",
"Crossvalidation time: 24.062s\n",
"\n",
"SVM C = 50.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 200.000000\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"SVM (scaled) MSE: 10840.248258\n",
"SVM (scaled) RMSE: 104.116513\n",
"SVM (scaled) MAE: 63.943106\n",
"SVM (scaled) MAEP: 0.080693\n",
"SVM (scaled) MBE: -9.794385\n",
"SVM (scaled) MBEP: -0.036205\n",
"SVM (scaled) R2: 0.829087\n",
"Crossvalidation time: 31.047s\n",
"\n",
"SVM C = 50.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 500.000000\n",
"SVM (scaled) MSE: 11281.266509\n",
"SVM (scaled) RMSE: 106.213307\n",
"SVM (scaled) MAE: 64.870725\n",
"SVM (scaled) MAEP: 0.081912\n",
"SVM (scaled) MBE: -9.327964\n",
"SVM (scaled) MBEP: -0.035956\n",
"SVM (scaled) R2: 0.822134\n",
"Crossvalidation time: 53.531s\n",
"\n",
"SVM C = 50.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 700.000000\n",
"SVM (scaled) MSE: 11805.049921\n",
"SVM (scaled) RMSE: 108.651047\n",
"SVM (scaled) MAE: 66.021563\n",
"SVM (scaled) MAEP: 0.083434\n",
"SVM (scaled) MBE: -9.156888\n",
"SVM (scaled) MBEP: -0.036030\n",
"SVM (scaled) R2: 0.813875\n",
"Crossvalidation time: 65.179s\n",
"\n",
"SVM C = 50.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 1000.000000\n",
"SVM (scaled) MSE: 12666.948816\n",
"SVM (scaled) RMSE: 112.547540\n",
"SVM (scaled) MAE: 67.767940\n",
"SVM (scaled) MAEP: 0.085423\n",
"SVM (scaled) MBE: -8.613697\n",
"SVM (scaled) MBEP: -0.035779\n",
"SVM (scaled) R2: 0.800286\n",
"Crossvalidation time: 86.382s\n",
"\n",
"SVM C = 70.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 10.000000\n",
"SVM (scaled) MSE: 12172.011389\n",
"SVM (scaled) RMSE: 110.326839\n",
"SVM (scaled) MAE: 69.911192\n",
"SVM (scaled) MAEP: 0.087558\n",
"SVM (scaled) MBE: -10.066645\n",
"SVM (scaled) MBEP: -0.038443\n",
"SVM (scaled) R2: 0.808090\n",
"Crossvalidation time: 18.239s\n",
"\n",
"SVM C = 70.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 20.000000\n",
"SVM (scaled) MSE: 11443.942715\n",
"SVM (scaled) RMSE: 106.976365\n",
"SVM (scaled) MAE: 66.329836\n",
"SVM (scaled) MAEP: 0.083675\n",
"SVM (scaled) MBE: -10.256958\n",
"SVM (scaled) MBEP: -0.037469\n",
"SVM (scaled) R2: 0.819569\n",
"Crossvalidation time: 18.532s\n",
"\n",
"SVM C = 70.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 50.000000\n",
"SVM (scaled) MSE: 10949.559443\n",
"SVM (scaled) RMSE: 104.640143\n",
"SVM (scaled) MAE: 64.379997\n",
"SVM (scaled) MAEP: 0.081304\n",
"SVM (scaled) MBE: -10.247675\n",
"SVM (scaled) MBEP: -0.037017\n",
"SVM (scaled) R2: 0.827363\n",
"Crossvalidation time: 22.836s\n",
"\n",
"SVM C = 70.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 70.000000\n",
"SVM (scaled) MSE: 10840.568159\n",
"SVM (scaled) RMSE: 104.118049\n",
"SVM (scaled) MAE: 63.999670\n",
"SVM (scaled) MAEP: 0.080803\n",
"SVM (scaled) MBE: -10.009236\n",
"SVM (scaled) MBEP: -0.036624\n",
"SVM (scaled) R2: 0.829082\n",
"Crossvalidation time: 25.097s\n",
"\n",
"SVM C = 70.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 100.000000\n",
"SVM (scaled) MSE: 10781.897108\n",
"SVM (scaled) RMSE: 103.835914\n",
"SVM (scaled) MAE: 63.818455\n",
"SVM (scaled) MAEP: 0.080553\n",
"SVM (scaled) MBE: -9.946328\n",
"SVM (scaled) MBEP: -0.036454\n",
"SVM (scaled) R2: 0.830007\n",
"Crossvalidation time: 30.340s\n",
"\n",
"SVM C = 70.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 200.000000\n",
"SVM (scaled) MSE: 10862.361042\n",
"SVM (scaled) RMSE: 104.222651\n",
"SVM (scaled) MAE: 64.006754\n",
"SVM (scaled) MAEP: 0.080775\n",
"SVM (scaled) MBE: -9.782864\n",
"SVM (scaled) MBEP: -0.036222\n",
"SVM (scaled) R2: 0.828738\n",
"Crossvalidation time: 41.067s\n",
"\n",
"SVM C = 70.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 500.000000\n",
"SVM (scaled) MSE: 11423.365235\n",
"SVM (scaled) RMSE: 106.880144\n",
"SVM (scaled) MAE: 65.186185\n",
"SVM (scaled) MAEP: 0.082339\n",
"SVM (scaled) MBE: -9.330777\n",
"SVM (scaled) MBEP: -0.035979\n",
"SVM (scaled) R2: 0.819893\n",
"Crossvalidation time: 71.186s\n",
"\n",
"SVM C = 70.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 700.000000\n",
"SVM (scaled) MSE: 12060.726055\n",
"SVM (scaled) RMSE: 109.821337\n",
"SVM (scaled) MAE: 66.522918\n",
"SVM (scaled) MAEP: 0.084111\n",
"SVM (scaled) MBE: -9.160439\n",
"SVM (scaled) MBEP: -0.036178\n",
"SVM (scaled) R2: 0.809844\n",
"Crossvalidation time: 89.224s\n",
"\n",
"SVM C = 70.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 1000.000000\n",
"SVM (scaled) MSE: 13065.320025\n",
"SVM (scaled) RMSE: 114.303631\n",
"SVM (scaled) MAE: 68.417957\n",
"SVM (scaled) MAEP: 0.086360\n",
"SVM (scaled) MBE: -8.498086\n",
"SVM (scaled) MBEP: -0.035870\n",
"SVM (scaled) R2: 0.794005\n",
"Crossvalidation time: 114.653s\n",
"\n",
"SVM C = 100.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 10.000000\n",
"SVM (scaled) MSE: 12068.887102\n",
"SVM (scaled) RMSE: 109.858487\n",
"SVM (scaled) MAE: 69.314605\n",
"SVM (scaled) MAEP: 0.086941\n",
"SVM (scaled) MBE: -9.896917\n",
"SVM (scaled) MBEP: -0.037877\n",
"SVM (scaled) R2: 0.809716\n",
"Crossvalidation time: 17.971s\n",
"\n",
"SVM C = 100.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 20.000000\n",
"SVM (scaled) MSE: 11365.286438\n",
"SVM (scaled) RMSE: 106.608097\n",
"SVM (scaled) MAE: 65.990980\n",
"SVM (scaled) MAEP: 0.083288\n",
"SVM (scaled) MBE: -10.236784\n",
"SVM (scaled) MBEP: -0.037318\n",
"SVM (scaled) R2: 0.820809\n",
"Crossvalidation time: 19.859s\n",
"\n",
"SVM C = 100.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 50.000000\n",
"SVM (scaled) MSE: 10923.316447\n",
"SVM (scaled) RMSE: 104.514671\n",
"SVM (scaled) MAE: 64.283194\n",
"SVM (scaled) MAEP: 0.081201\n",
"SVM (scaled) MBE: -10.255503\n",
"SVM (scaled) MBEP: -0.036982\n",
"SVM (scaled) R2: 0.827777\n",
"Crossvalidation time: 25.813s\n",
"\n",
"SVM C = 100.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 70.000000\n",
"SVM (scaled) MSE: 10815.077454\n",
"SVM (scaled) RMSE: 103.995565\n",
"SVM (scaled) MAE: 63.921116\n",
"SVM (scaled) MAEP: 0.080694\n",
"SVM (scaled) MBE: -9.935495\n",
"SVM (scaled) MBEP: -0.036509\n",
"SVM (scaled) R2: 0.829484\n",
"Crossvalidation time: 28.104s\n",
"\n",
"SVM C = 100.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 100.000000\n",
"SVM (scaled) MSE: 10782.976938\n",
"SVM (scaled) RMSE: 103.841114\n",
"SVM (scaled) MAE: 63.830678\n",
"SVM (scaled) MAEP: 0.080607\n",
"SVM (scaled) MBE: -9.886186\n",
"SVM (scaled) MBEP: -0.036387\n",
"SVM (scaled) R2: 0.829990\n",
"Crossvalidation time: 33.360s\n",
"\n",
"SVM C = 100.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 200.000000\n",
"SVM (scaled) MSE: 10878.555987\n",
"SVM (scaled) RMSE: 104.300316\n",
"SVM (scaled) MAE: 64.068177\n",
"SVM (scaled) MAEP: 0.080837\n",
"SVM (scaled) MBE: -9.687263\n",
"SVM (scaled) MBEP: -0.036138\n",
"SVM (scaled) R2: 0.828483\n",
"Crossvalidation time: 48.447s\n",
"\n",
"SVM C = 100.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 500.000000\n",
"SVM (scaled) MSE: 11601.075669\n",
"SVM (scaled) RMSE: 107.708290\n",
"SVM (scaled) MAE: 65.591167\n",
"SVM (scaled) MAEP: 0.082884\n",
"SVM (scaled) MBE: -9.181940\n",
"SVM (scaled) MBEP: -0.035904\n",
"SVM (scaled) R2: 0.817091\n",
"Crossvalidation time: 100.223s\n",
"\n",
"SVM C = 100.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 700.000000\n",
"SVM (scaled) MSE: 12367.141411\n",
"SVM (scaled) RMSE: 111.207650\n",
"SVM (scaled) MAE: 67.066229\n",
"SVM (scaled) MAEP: 0.084827\n",
"SVM (scaled) MBE: -9.109264\n",
"SVM (scaled) MBEP: -0.036231\n",
"SVM (scaled) R2: 0.805013\n",
"Crossvalidation time: 119.214s\n",
"\n",
"SVM C = 100.000000\n",
"SVM epsilon = 0.100000\n",
"SVM gamma = 1000.000000\n",
"SVM (scaled) MSE: 13560.087904\n",
"SVM (scaled) RMSE: 116.447790\n",
"SVM (scaled) MAE: 69.264932\n",
"SVM (scaled) MAEP: 0.087524\n",
"SVM (scaled) MBE: -8.351864\n",
"SVM (scaled) MBEP: -0.035938\n",
"SVM (scaled) R2: 0.786205\n",
"Crossvalidation time: 154.301s\n"
]
}
],
"source": [
"name = 'SVM'\n",
"svm_tuning = pd.DataFrame(data = [], \n",
" columns = ['C', 'epsilon', 'gamma', 'scaled_target', 'time'])\n",
"\n",
"# SVM_gamma = 'scale'\n",
"# SVM_epsilon = 0.1\n",
"for SVM_epsilon in [0.01, 0.1]:\n",
" for C in [1, 2, 5, 7, 10, 20, 50, 70, 100]:\n",
" for SVM_gamma in [10, 20, 50, 70, 100, 200, 500, 700, 1000]: \n",
" print('\\nSVM C = %f' %C)\n",
" print('SVM epsilon = %f' %SVM_epsilon)\n",
" print('SVM gamma = %f' %SVM_gamma)\n",
" tt = time.time()\n",
"\n",
" est = SVR( C = C, kernel = 'rbf', epsilon = SVM_epsilon, gamma = SVM_gamma)\n",
" y = cross_val_predict(est, x, t, cv = 5)\n",
" Y = rescale(y, T)\n",
" scaled_err = get_errors( shp(T), shp(Y), 'SVM (scaled)' )\n",
"\n",
" cv_time = time.time()-tt\n",
" print('Crossvalidation time: %.3fs' %cv_time)\n",
"\n",
" svm_tuning = svm_tuning.append( pd.DataFrame(data = [[ C, SVM_epsilon, SVM_gamma, \n",
" scaled_err.RMSE, cv_time]], \n",
" columns = ['C', 'epsilon', 'gamma', \n",
" 'scaled_target', 'time']) )"
]
},
{
"cell_type": "code",
"execution_count": 143,
"metadata": {},
"outputs": [],
"source": [
"svm_tuning_xr = svm_tuning.set_index(['C', 'epsilon', 'gamma']).to_xarray()"
]
},
{
"cell_type": "code",
"execution_count": 144,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (C: 9, epsilon: 2, gamma: 9)\n",
"Coordinates:\n",
" * C (C) int64 1 2 5 7 10 20 50 70 100\n",
" * epsilon (epsilon) float64 0.01 0.1\n",
" * gamma (gamma) int64 10 20 50 70 100 200 500 700 1000\n",
"Data variables:\n",
" scaled_target (C, epsilon, gamma) float64 142.3 123.3 108.4 ... 111.2 116.4\n",
" time (C, epsilon, gamma) float64 18.18 19.09 17.98 ... 119.2 154.3"
]
},
"execution_count": 144,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"svm_tuning_xr"
]
},
{
"cell_type": "code",
"execution_count": 145,
"metadata": {},
"outputs": [],
"source": [
"svm_tuning_xr.to_netcdf('svm_tuning_10k.nc')"
]
},
{
"cell_type": "code",
"execution_count": 146,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.DataArray 'scaled_target' ()>\n",
"array(103.78608)"
]
},
"execution_count": 146,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"svm_tuning_xr.scaled_target.min()"
]
},
{
"cell_type": "code",
"execution_count": 155,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (C: 9, epsilon: 1, gamma: 4)\n",
"Coordinates:\n",
" * C (C) int64 1 2 5 7 10 20 50 70 100\n",
" * epsilon (epsilon) float64 0.1\n",
" * gamma (gamma) int64 70 100 200 500\n",
"Data variables:\n",
" scaled_target (C, epsilon, gamma) float64 nan nan nan ... 103.8 nan nan\n",
" time (C, epsilon, gamma) float64 nan nan nan ... 33.36 nan nan"
]
},
"execution_count": 155,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"svm_tuning_xr.where(svm_tuning_xr.scaled_target < 104, drop = True)"
]
},
{
"cell_type": "code",
"execution_count": 156,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[[ nan, nan, nan, 103.85338758]],\n",
"\n",
" [[ nan, nan, nan, 103.9970967 ]],\n",
"\n",
" [[ nan, nan, 103.81833488, nan]],\n",
"\n",
" [[ nan, nan, 103.789383 , nan]],\n",
"\n",
" [[ nan, nan, 103.78607998, nan]],\n",
"\n",
" [[ nan, nan, 103.9042854 , nan]],\n",
"\n",
" [[ nan, 103.86051825, nan, nan]],\n",
"\n",
" [[ nan, 103.83591435, nan, nan]],\n",
"\n",
" [[103.99556459, 103.84111391, nan, nan]]])"
]
},
"execution_count": 156,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"svm_tuning_xr.where(svm_tuning_xr.scaled_target < 104, drop = True).drop('epsilon').scaled_target.values"
]
},
{
"cell_type": "code",
"execution_count": 149,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" if (mpl.ratio != 1) {\n",
" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
" }\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" fig.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"svm_tuning_xr.scaled_target.attrs['long_name'] = 'RMSE ($kWh/m^2$)'\n",
"\n",
"svm_tuning_xr.scaled_target.isel(epsilon = i).plot(cmap = 'coolwarm') # vmin = 1.9, vmax = 2.4\n",
" \n",
"plt.xscale('log')\n",
"plt.xlim(5, 1e3)\n",
"plt.xlabel('Kernel coefficient ($\\gamma$)')\n",
"\n",
"plt.yscale('log')\n",
"plt.ylim(0.5, 1e2)\n",
"plt.ylabel('Error penalization term ($C$)')\n",
"\n",
"plt.title('')\n",
"plt.tight_layout()\n",
"plt.savefig('SVM_tuning_10k.png', dpi = 300)"
]
},
{
"cell_type": "code",
"execution_count": 152,
"metadata": {},
"outputs": [
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" if (mpl.ratio != 1) {\n",
" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
" }\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" fig.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\" width=\"1000\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize = (10, 8))\n",
"for i in range(2):\n",
" plt.subplot(2,2,(2*i+1))\n",
" p1 = np.log10(svm_tuning_xr.scaled_target.isel(epsilon = i)).plot(vmax = 2.1) # vmin = 1.9, vmax = 2.4\n",
" plt.xscale('log')\n",
" plt.xlim(5, 1e3)\n",
"\n",
" plt.yscale('log')\n",
" plt.ylim(0.5, 1e2)\n",
"\n",
" plt.subplot(2,2, (2*i+2))\n",
" p2 = np.log10(svm_tuning_xr.time.isel(epsilon = i)).plot(cmap = 'RdBu_r') # vmin = -1.5, vmax = 1.5\n",
" plt.xscale('log')\n",
" plt.xlim(5, 1e3)\n",
"\n",
" plt.yscale('log')\n",
" plt.ylim(0.5, 1e2)\n",
" \n",
"plt.tight_layout()\n",
"plt.savefig('SVF_tuning_round2_10k.png', dpi = 300)"
]
},
{
"cell_type": "code",
"execution_count": 132,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" if (mpl.ratio != 1) {\n",
" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
" }\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" fig.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<img src=\"data:image/png;base64,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\" 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],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize = (10, 8))\n",
"for i in range(2):\n",
" plt.subplot(2,2,(2*i+1))\n",
" p1 = np.log10(svm_tuning_xr.scaled_target.isel(epsilon = i)).plot(vmax = 2.1) # vmin = 1.9, vmax = 2.4\n",
" plt.xscale('log')\n",
" plt.xlim(5, 1e3)\n",
"\n",
" plt.yscale('log')\n",
" plt.ylim(0.5, 1e2)\n",
"\n",
" plt.subplot(2,2, (2*i+2))\n",
" p2 = np.log10(svm_tuning_xr.time.isel(epsilon = i)).plot() # vmin = -1.5, vmax = 1.5\n",
" plt.xscale('log')\n",
" plt.xlim(5, 1e3)\n",
"\n",
" plt.yscale('log')\n",
" plt.ylim(0.5, 1e2)\n",
" \n",
"plt.tight_layout()\n",
"plt.savefig('SVF_tuning_round2.png', dpi = 300)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# CURRENTLY NOT USED\n",
"tt = time.time()\n",
"\n",
" est = SVR( C = C, kernel = 'rbf', epsilon = SVM_epsilon, gamma = SVM_gamma)\n",
" Y = cross_val_predict(est, X, T, cv = 5)\n",
" ununscaled_err = get_errors( shp(T), shp(Y), 'SVM (unscaled)' )\n",
"\n",
" print('Crossvalidation time: %.3fs' %(time.time()-tt))\n",
" tt = time.time()\n",
"\n",
" est = SVR( C = C, kernel = 'rbf', epsilon = SVM_epsilon, gamma = SVM_gamma)\n",
" Y = cross_val_predict(est, x, T, cv = 5)\n",
" unscaled_err = get_errors( shp(T), shp(Y), 'SVM (unscaled)' )\n",
"\n",
" print('Crossvalidation time: %.3fs' %(time.time()-tt))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plot 2D histogram"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.linear_model import LinearRegression\n",
"\n",
"min_val = max(T_tst.min(), Y_tst.min())\n",
"max_val = min(T_tst.max(), Y_tst.max())\n",
"\n",
"X_regr = Y_tst.reshape(-1, 1)\n",
"Y_regr = T_tst.reshape(-1, 1)\n",
"\n",
"LIN = LinearRegression( n_jobs = -1 )\n",
"LIN.fit( X_regr, Y_regr )\n",
"\n",
"x_regr = np.asarray([min_val, max_val]).reshape(-1, 1)\n",
"y_regr = LIN.predict( x_regr )\n",
"\n",
"R2 = LIN.score(X_regr, Y_regr)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"min_val = 500\n",
"max_val = 1700\n",
"\n",
"plt.figure()\n",
"\n",
"plt.hist2d( Y_tst, T_tst, bins=100, cmap = 'Oranges')\n",
"plt.plot([min_val,max_val],[min_val,max_val], c = 'grey', alpha= 0.7, lw = 0.7)\n",
"plt.plot(x_regr, y_regr, c = 'k', lw = 0.7, label = 'Regression line ($R^2 = %.2f$)' %R2)\n",
"# plt.plot([], [], c = 'grew', ls = '--', lw = 0.7, alpha = 0.7)\n",
"\n",
"plt.ylabel('Target ($kWh/m^2$)')\n",
"plt.xlabel('Prediction ($kWh/m^2$)')\n",
"# plt.title('Annual solar irradiation')\n",
"\n",
"plt.axis('equal')\n",
"plt.xlim((min_val, max_val))\n",
"plt.ylim((min_val, max_val))\n",
"\n",
"cbar = plt.colorbar()\n",
"cbar.set_label('# of values in bin')\n",
"\n",
"plt.legend()\n",
"plt.tight_layout()\n",
"plt.show()\n",
"# plt.savefig('Regression_1M.png', dpi = 300)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Double-check feature importance"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# for standard estimators\n",
"n_features = X.shape[1]\n",
"importance = np.mean([tree.feature_importances_ for tree in RF.estimators_],axis=0)\n",
"std = np.std([tree.feature_importances_ for tree in RF.estimators_],axis=0)\n",
"ind = np.argsort(importance)[-n_features:]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(importance)\n",
"print(std)\n",
"print(ind)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.figure()\n",
"ax = plt.axes()\n",
"# plt.title(\"Feature Importance for RF (%dk features)\" %(X.shape[0]/1000)) # \\n Testing RMSE: %f ; , np.sqrt(mse(Y_te,T_te))\n",
"plt.barh(range(n_features),importance[ind],\n",
" color=\"C1\", xerr=std[ind], alpha=0.5, align=\"center\", capsize = 2)\n",
"plt.yticks(range(n_features), np.asarray(feature_labels)[ind])\n",
"plt.ylim([-1, len(ind)])\n",
"plt.xlabel('Feature importance')\n",
"plt.tight_layout()\n",
"plt.show()\n",
"# plt.savefig('Feature_importance_selFtrs_mean_10k.png', dpi=300)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# ML with uncertainty"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"estimator = set_estimators()\n",
"RF_unc = Uncertainty( estimator['RF'] )\n",
"RF_unc.fit(X, T)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model variance not given\n",
"Estimating model variance ... \n",
"Fitting model ...\n",
"CPU time: 228.80 seconds\n",
"Wall clock time: 82.86 seconds\n"
]
}
],
"source": [
"# set residual estimator and fit \n",
"RF_unc.residuals.set_params( n_estimators = n_est )\n",
"\n",
"tt = util.Timer()\n",
"residuals = RF_unc.fit_residuals( X, T )\n",
"tt.stop()"
]
},
{
"cell_type": "code",
"execution_count": 204,
"metadata": {
"collapsed": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU time: 200.51 seconds\n",
"Wall clock time: 125.80 seconds\n"
]
},
{
"ename": "NameError",
"evalue": "name 'compute_residuals' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-204-4816131a1aa3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mtt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mRes_tst\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompute_residuals\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mshp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mY_tst\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mT_tst\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmUnc_tst\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'compute_residuals' is not defined"
]
}
],
"source": [
"# get prediction, model uncertainty and data uncertainty\n",
"tt = util.Timer()\n",
"Y_tst, mUnc_tst = RF_unc.predict_model_uncertainty(X_tst)\n",
"dUnc_tst = RF_unc.predict_residuals(X_tst)\n",
"tt.stop()"
]
},
{
"cell_type": "code",
"execution_count": 208,
"metadata": {},
"outputs": [],
"source": [
"Res_tst = compute_residuals( shp(Y_tst), shp(T_tst), shp(mUnc_tst) )"
]
},
{
"cell_type": "code",
"execution_count": 213,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Prediction MSE: 8976.326644\n",
"Prediction RMSE: 94.743478\n",
"Prediction MAE: 58.576197\n",
"Prediction MAEP: 0.076631\n",
"Prediction MBE: -15.192731\n",
"Prediction MBEP: -0.038931\n",
"Prediction R2: 0.836498\n",
"\n",
"Residuals MSE: 4994.676687\n",
"Residuals RMSE: 70.673027\n",
"Residuals MAE: 40.612807\n",
"Residuals MAEP: 1.647667\n",
"Residuals MBE: 2.376570\n",
"Residuals MBEP: -0.149421\n",
"Residuals R2: -7.559683\n"
]
}
],
"source": [
"pred_err = get_errors( shp(T_tst), shp(Y_tst), 'Prediction' )\n",
"print('')\n",
"res_err = get_errors( shp(dUnc_tst), shp(Res_tst), 'Residuals' )"
]
},
{
"cell_type": "code",
"execution_count": 214,
"metadata": {},
"outputs": [],
"source": [
"# merge all test results into one dataframe\n",
"identifier = 'DF_UID'\n",
"identifiers = shp(testing.index.values)\n",
"test_results = pd.DataFrame( np.hstack([identifiers, shp(T_tst), shp(Y_tst), shp(mUnc_tst), shp(dUnc_tst) ]), \n",
" columns = ['DF_UID', 'target', 'prediction', 'model_std', 'data_std'])\n",
"test_results['total_std'] = np.sqrt(test_results.model_std ** 2 + test_results.data_std ** 2)\n",
"test_results['data_std_rel_to_total'] = test_results['total_std'] * test_results['data_std'] / ( test_results['data_std'] + test_results['model_std'] )\n",
"test_results['model_std_rel_to_total'] = test_results['total_std'] * test_results['model_std'] / ( test_results['data_std'] + test_results['model_std'] )"
]
},
{
"cell_type": "code",
"execution_count": 247,
"metadata": {},
"outputs": [
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" if (mpl.ratio != 1) {\n",
" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
" }\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" fig.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
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" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
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" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
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" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
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"\n",
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"\n",
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"\n",
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"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
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"\n",
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"\n",
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" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
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" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# prepare sample for plotting\n",
"SAMPLE_LENGTH = 50\n",
"test_sample = test_results.sample( SAMPLE_LENGTH ).sort_values(by = 'prediction', ascending = False).reset_index()\n",
"\n",
"# plot results\n",
"plt.figure()\n",
"test_sample.prediction.plot( kind = 'bar', color = 'C0', width = 0.9, alpha = 0.3)\n",
"ticks, labels = plt.xticks( range(0, SAMPLE_LENGTH, 10), range(0, SAMPLE_LENGTH, 10))\n",
"\n",
"plt.errorbar( range(SAMPLE_LENGTH), test_sample.prediction, yerr = test_sample.total_std, \n",
" color = 'k', elinewidth = 1, capsize=1.5, \n",
" fmt = '.', markersize = 1, \n",
" label = 'Data uncertainty')\n",
"\n",
"plt.errorbar( range(SAMPLE_LENGTH), test_sample.prediction, yerr = test_sample.model_std_rel_to_total, \n",
" color = 'r', elinewidth = 1, capsize=1.5, \n",
" fmt = '.', markersize = 1, \n",
" label = 'Model uncertainty')\n",
"\n",
"\n",
"test_sample.target.plot( style = 'go', alpha = 0.5 )\n",
"# plt.title('Prediction and uncertainty of solar irradiation')\n",
"plt.legend(loc = 'upper right')\n",
"plt.xlabel('Sample (randomly selected)')\n",
"plt.ylabel('Prediction (in $kWh / m^2)$')\n",
"plt.xlim((-1, 50))\n",
"\n",
"plt.tight_layout()\n",
"# plt.savefig( os.path.join( 'pred_uncertainty_irrad_tst_new.png'), dpi = 300)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Tilt and aspect patterns for predicted values and uncertainty"
]
},
{
"cell_type": "code",
"execution_count": 266,
"metadata": {},
"outputs": [],
"source": [
"merged_results = test_results.merge(testing[['NEIGUNG', 'AUSRICHTUNG']], \n",
" left_on = 'DF_UID', right_index = True, how = 'left')"
]
},
{
"cell_type": "code",
"execution_count": 267,
"metadata": {},
"outputs": [],
"source": [
"merged_results['total_std_perc'] = merged_results.total_std / merged_results.prediction"
]
},
{
"cell_type": "code",
"execution_count": 268,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>DF_UID</th>\n",
" <th>target</th>\n",
" <th>prediction</th>\n",
" <th>model_std</th>\n",
" <th>data_std</th>\n",
" <th>total_std</th>\n",
" <th>data_std_rel_to_total</th>\n",
" <th>model_std_rel_to_total</th>\n",
" <th>NEIGUNG</th>\n",
" <th>AUSRICHTUNG</th>\n",
" <th>total_std_perc</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1001041.0</td>\n",
" <td>1124.0</td>\n",
" <td>1030.105617</td>\n",
" <td>142.829931</td>\n",
" <td>61.922971</td>\n",
" <td>155.675442</td>\n",
" <td>47.080583</td>\n",
" <td>108.594860</td>\n",
" <td>1</td>\n",
" <td>-51.0</td>\n",
" <td>0.151126</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>8720624.0</td>\n",
" <td>1431.0</td>\n",
" <td>1371.053704</td>\n",
" <td>59.213866</td>\n",
" <td>25.345624</td>\n",
" <td>64.410268</td>\n",
" <td>19.306153</td>\n",
" <td>45.104115</td>\n",
" <td>41</td>\n",
" <td>6.0</td>\n",
" <td>0.046979</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2671377.0</td>\n",
" <td>867.0</td>\n",
" <td>931.658432</td>\n",
" <td>75.201291</td>\n",
" <td>22.660883</td>\n",
" <td>78.541389</td>\n",
" <td>18.186978</td>\n",
" <td>60.354411</td>\n",
" <td>28</td>\n",
" <td>-128.0</td>\n",
" <td>0.084303</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>8826005.0</td>\n",
" <td>1337.0</td>\n",
" <td>1320.957118</td>\n",
" <td>70.414994</td>\n",
" <td>32.842632</td>\n",
" <td>77.697554</td>\n",
" <td>24.712869</td>\n",
" <td>52.984685</td>\n",
" <td>30</td>\n",
" <td>18.0</td>\n",
" <td>0.058819</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>3008353.0</td>\n",
" <td>1262.0</td>\n",
" <td>1206.295618</td>\n",
" <td>123.036708</td>\n",
" <td>48.113745</td>\n",
" <td>132.109666</td>\n",
" <td>37.138615</td>\n",
" <td>94.971051</td>\n",
" <td>41</td>\n",
" <td>-1.0</td>\n",
" <td>0.109517</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" DF_UID target prediction model_std data_std total_std \\\n",
"0 1001041.0 1124.0 1030.105617 142.829931 61.922971 155.675442 \n",
"1 8720624.0 1431.0 1371.053704 59.213866 25.345624 64.410268 \n",
"2 2671377.0 867.0 931.658432 75.201291 22.660883 78.541389 \n",
"3 8826005.0 1337.0 1320.957118 70.414994 32.842632 77.697554 \n",
"4 3008353.0 1262.0 1206.295618 123.036708 48.113745 132.109666 \n",
"\n",
" data_std_rel_to_total model_std_rel_to_total NEIGUNG AUSRICHTUNG \\\n",
"0 47.080583 108.594860 1 -51.0 \n",
"1 19.306153 45.104115 41 6.0 \n",
"2 18.186978 60.354411 28 -128.0 \n",
"3 24.712869 52.984685 30 18.0 \n",
"4 37.138615 94.971051 41 -1.0 \n",
"\n",
" total_std_perc \n",
"0 0.151126 \n",
"1 0.046979 \n",
"2 0.084303 \n",
"3 0.058819 \n",
"4 0.109517 "
]
},
"execution_count": 268,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"merged_results.head()"
]
},
{
"cell_type": "code",
"execution_count": 269,
"metadata": {},
"outputs": [],
"source": [
"# ADJUST FOR FLAT ROOFS\n",
"FLAT = -5.1\n",
"merged_results['tilt'] = merged_results.NEIGUNG\n",
"merged_results.loc[merged_results.NEIGUNG == 0, 'tilt'] = FLAT # NEEDED FOR PLOTTING"
]
},
{
"cell_type": "code",
"execution_count": 270,
"metadata": {},
"outputs": [],
"source": [
"ASP_ROUND = 10\n",
"TILT_ROUND = 10\n",
"merged_results['tilt_round'] = util.round_to_interval(merged_results.tilt, TILT_ROUND)\n",
"merged_results['aspect_round'] = util.round_to_interval(merged_results.AUSRICHTUNG, ASP_ROUND)"
]
},
{
"cell_type": "code",
"execution_count": 271,
"metadata": {},
"outputs": [],
"source": [
"results_mean = merged_results.groupby(['tilt_round', 'aspect_round']).mean()"
]
},
{
"cell_type": "code",
"execution_count": 276,
"metadata": {},
"outputs": [
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" if (mpl.ratio != 1) {\n",
" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
" }\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" fig.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
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},
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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_var = results_mean['prediction']\n",
"plot_array = plot_var.to_xarray()\n",
"\n",
"plot_array['tilt_round' == FLAT] = plot_array['tilt_round' == FLAT].mean()\n",
"\n",
"# prepare data\n",
"data = plot_array.values\n",
"# theta = np.append(plot_array.aspect_round * np.pi / 180, 2*np.pi) + (0.5 * ASP_ROUND * np.pi / 180)\n",
"theta = plot_array.aspect_round * np.pi / 180 - (0.5 * ASP_ROUND * np.pi / 180)\n",
"tilts = plot_array.tilt_round\n",
"\n",
"X, Y = np.meshgrid( theta, tilts )\n",
"\n",
"# SET PROPERTIES\n",
"VMIN = None\n",
"VMAX = None\n",
" \n",
"plt.figure()\n",
"ax = plt.subplot(projection='polar')\n",
"ax.set_theta_zero_location('S')\n",
"ax.set_theta_direction(-1)\n",
"# ax.set_rlim(-10, 70)\n",
"\n",
"plt.pcolor(X, Y, data, cmap = cm_PV, vmin = VMIN, vmax = VMAX)\n",
"cb = plt.colorbar() # extend = get_cbar_extent( data, VMIN, VMAX )\n",
"cb.set_label('Predicted annual $G_t$ (in $kWh/m^2$)')\n",
"\n",
"# FORMATTING\n",
"xticks = ['S', 'SW', 'W', 'NW', 'N', 'NE', 'E', 'SE']\n",
"yticks = [str(int(tilt)) + '°' for tilt in ax.get_yticks()]\n",
"ax.set_xticklabels( xticks )\n",
"ax.set_yticklabels( yticks )\n",
"ax.text(60,80, 'Tilt angle')\n",
"ax.set_rlabel_position(200) # get radial labels away from plotted line\n",
"\n",
"plt.tight_layout()\n",
"plt.show()\n",
"plt.savefig('prediction_tilt_aspect.png', dpi = 300)"
]
},
{
"cell_type": "code",
"execution_count": 263,
"metadata": {},
"outputs": [
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" if (mpl.ratio != 1) {\n",
" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
" }\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" fig.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_var = results_mean['target']\n",
"plot_array = plot_var.to_xarray()\n",
"\n",
"plot_array['tilt_round' == FLAT] = plot_array['tilt_round' == FLAT].mean()\n",
"\n",
"# prepare data\n",
"data = plot_array.values\n",
"# theta = np.append(plot_array.aspect_round * np.pi / 180, 2*np.pi) + (0.5 * ASP_ROUND * np.pi / 180)\n",
"theta = plot_array.aspect_round * np.pi / 180 - (0.5 * ASP_ROUND * np.pi / 180)\n",
"tilts = plot_array.tilt_round\n",
"\n",
"X, Y = np.meshgrid( theta, tilts )\n",
"\n",
"# SET PROPERTIES\n",
"VMIN = None\n",
"VMAX = None\n",
" \n",
"plt.figure()\n",
"ax = plt.subplot(projection='polar')\n",
"ax.set_theta_zero_location('S')\n",
"ax.set_theta_direction(-1)\n",
"# ax.set_rlim(-10, 70)\n",
"\n",
"plt.pcolor(X, Y, data, cmap = cm_PV, vmin = VMIN, vmax = VMAX)\n",
"cb = plt.colorbar() # extend = get_cbar_extent( data, VMIN, VMAX )\n",
"cb.set_label('Target annual $G_t$ ($kWh/m^2$)')\n",
"\n",
"# FORMATTING\n",
"xticks = ['S', 'SW', 'W', 'NW', 'N', 'NE', 'E', 'SE']\n",
"yticks = [str(int(tilt)) + '°' for tilt in ax.get_yticks()]\n",
"ax.set_xticklabels( xticks )\n",
"ax.set_yticklabels( yticks )\n",
"ax.text(60,80, 'Tilt angle')\n",
"ax.set_rlabel_position(200) # get radial labels away from plotted line\n",
"\n",
"plt.tight_layout()\n",
"plt.show()\n",
"plt.savefig('target_tilt_aspect.png', dpi = 300)"
]
},
{
"cell_type": "code",
"execution_count": 274,
"metadata": {},
"outputs": [
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" if (mpl.ratio != 1) {\n",
" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
" }\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" fig.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_var = results_mean['total_std_perc'] * 100\n",
"plot_array = plot_var.to_xarray()\n",
"\n",
"plot_array['tilt_round' == FLAT] = plot_array['tilt_round' == FLAT].mean()\n",
"\n",
"# prepare data\n",
"data = plot_array.values\n",
"# theta = np.append(plot_array.aspect_round * np.pi / 180, 2*np.pi) + (0.5 * ASP_ROUND * np.pi / 180)\n",
"theta = plot_array.aspect_round * np.pi / 180 - (0.5 * ASP_ROUND * np.pi / 180)\n",
"tilts = plot_array.tilt_round\n",
"\n",
"X, Y = np.meshgrid( theta, tilts )\n",
"\n",
"# SET PROPERTIES\n",
"VMIN = None\n",
"VMAX = None\n",
" \n",
"plt.figure()\n",
"ax = plt.subplot(projection='polar')\n",
"ax.set_theta_zero_location('S')\n",
"ax.set_theta_direction(-1)\n",
"# ax.set_rlim(-10, 70)\n",
"\n",
"plt.pcolor(X, Y, data, cmap = cm_PV, vmin = VMIN, vmax = VMAX)\n",
"cb = plt.colorbar() # extend = get_cbar_extent( data, VMIN, VMAX )\n",
"cb.set_label('Relative uncertainty for $G_t$ (in %)')\n",
"\n",
"# FORMATTING\n",
"xticks = ['S', 'SW', 'W', 'NW', 'N', 'NE', 'E', 'SE']\n",
"yticks = [str(int(tilt)) + '°' for tilt in ax.get_yticks()]\n",
"ax.set_xticklabels( xticks )\n",
"ax.set_yticklabels( yticks )\n",
"ax.text(60,80, 'Tilt angle')\n",
"ax.set_rlabel_position(200) # get radial labels away from plotted line\n",
"\n",
"plt.tight_layout()\n",
"plt.show()\n",
"plt.savefig('total_std_perc_tilt_aspect.png', dpi = 300)"
]
},
{
"cell_type": "code",
"execution_count": 275,
"metadata": {},
"outputs": [
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" if (mpl.ratio != 1) {\n",
" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
" }\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" fig.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<img 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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_var = results_mean['total_std']\n",
"plot_array = plot_var.to_xarray()\n",
"\n",
"plot_array['tilt_round' == FLAT] = plot_array['tilt_round' == FLAT].mean()\n",
"\n",
"# prepare data\n",
"data = plot_array.values\n",
"# theta = np.append(plot_array.aspect_round * np.pi / 180, 2*np.pi) + (0.5 * ASP_ROUND * np.pi / 180)\n",
"theta = plot_array.aspect_round * np.pi / 180 - (0.5 * ASP_ROUND * np.pi / 180)\n",
"tilts = plot_array.tilt_round\n",
"\n",
"X, Y = np.meshgrid( theta, tilts )\n",
"\n",
"# SET PROPERTIES\n",
"VMIN = None\n",
"VMAX = None\n",
" \n",
"plt.figure()\n",
"ax = plt.subplot(projection='polar')\n",
"ax.set_theta_zero_location('S')\n",
"ax.set_theta_direction(-1)\n",
"# ax.set_rlim(-10, 70)\n",
"\n",
"plt.pcolor(X, Y, data, cmap = cm_PV, vmin = VMIN, vmax = VMAX)\n",
"cb = plt.colorbar() # extend = get_cbar_extent( data, VMIN, VMAX )\n",
"cb.set_label('Uncertainty for $G_t$ (in $kWh/m^2$)')\n",
"\n",
"# FORMATTING\n",
"xticks = ['S', 'SW', 'W', 'NW', 'N', 'NE', 'E', 'SE']\n",
"yticks = [str(int(tilt)) + '°' for tilt in ax.get_yticks()]\n",
"ax.set_xticklabels( xticks )\n",
"ax.set_yticklabels( yticks )\n",
"ax.text(60,80, 'Tilt angle')\n",
"ax.set_rlabel_position(200) # get radial labels away from plotted line\n",
"\n",
"plt.tight_layout()\n",
"plt.show()\n",
"plt.savefig('total_std_tilt_aspect.png', dpi = 300)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Predict for cities and compare potential"
]
},
{
"cell_type": "code",
"execution_count": 180,
"metadata": {},
"outputs": [],
"source": [
"x, t, X, T = get_data( training_data.sample(1e9), feature_names, label_name)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"estimator = set_estimators()\n",
"RF_unc = Uncertainty( estimator['RF'] )\n",
"RF_unc.fit(X, T)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model variance not given\n",
"Estimating model variance ... \n",
"Fitting model ...\n"
]
}
],
"source": [
"# set residual estimator and fit \n",
"RF_unc.residuals.set_params( n_estimators = n_est )\n",
"\n",
"tt = util.Timer()\n",
"residuals = RF_unc.fit_residuals( X, T )\n",
"tt.stop()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"city_pred = {}\n",
"for city in cities:\n",
" print(city)\n",
" # get prediction, model uncertainty and data uncertainty\n",
" x_tst, t_tst, X_tst, T_tst = get_data( city_data[city], feature_names, label_name)\n",
" \n",
" tt = util.Timer()\n",
" Y_tst, mUnc_tst = RF_unc.predict_model_uncertainty(X_tst)\n",
" dUnc_tst = RF_unc.predict_residuals(X_tst)\n",
" tt.stop()\n",
" \n",
" identifiers = shp(city_data[city].index.values)\n",
" test_results = pd.DataFrame( np.hstack([identifiers, shp(T_tst), shp(Y_tst), shp(mUnc_tst), shp(dUnc_tst) ]), \n",
" columns = ['DF_UID', 'target', 'prediction', 'model_std', 'data_std'])\n",
" \n",
" city_pred[city] = test_results"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### SELECT SUITABLE ROOFS & APPLY SUITABILITY CRITERIA"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"GWR_cols = pd.read_csv('/Users/alinawalch/Documents/EPFL/data/GWR/Maske_EINZELD-GEB.csv').columns\n",
"GWR = pd.read_csv('/Users/alinawalch/Documents/EPFL/data/GWR/EPFL-150701-GEB.txt', usecols = ['EGID', 'GKAT'],\n",
" header = None, names = GWR_cols, sep = '\\t', encoding = 'cp1250' )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# merge all info\n",
"for city in cities:\n",
" city_pred[ city ] = city_pred[ city ].merge(city_data[ city ][['FLAECHE', 'NEIGUNG', 'AUSRICHTUNG', 'GWR_EGID']], \n",
" left_on = 'DF_UID', right_index = True, how = 'left')\n",
" \n",
" city_pred[ city ] = city_pred[ city ].merge(GWR, left_on = 'GWR_EGID', right_on = 'EGID', how = 'left') "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"city_pred[ city ].head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"city_suitable = {}\n",
"\n",
"for city in cities:\n",
" suited_roofs = city_pred[ city ][ (city_pred[ city ].FLAECHE >= 10) & (city_pred[ city ].target >= 1000) ]\n",
"\n",
" # define rules for roof categories\n",
" flat_roofs = suited_roofs.NEIGUNG <= 10\n",
" steep_roofs = suited_roofs.NEIGUNG > 10\n",
"\n",
" large_roofs = (suited_roofs.NEIGUNG <= 10) & (suited_roofs.FLAECHE >= 1000)\n",
" small_roofs = (suited_roofs.NEIGUNG <= 10) & (suited_roofs.FLAECHE < 1000)\n",
"\n",
" MFH = (suited_roofs.NEIGUNG <= 10) & (suited_roofs.GKAT == 1025)\n",
"\n",
" # compute untilization factor\n",
" suited_roofs['UF'] = 0\n",
" suited_roofs.loc[steep_roofs, 'UF'] = 0.7\n",
"\n",
" # for flat roofs, different logic applies for small and large roofs, and for multi-family-houses\n",
" suited_roofs.loc[small_roofs, 'UF'] = 0.7\n",
" suited_roofs.loc[large_roofs, 'UF'] = 0.8\n",
"\n",
" suited_roofs.loc[MFH, 'UF'] = suited_roofs[MFH].UF * 0.6\n",
"\n",
" suited_roofs['available_area'] = suited_roofs.UF * suited_roofs.FLAECHE\n",
"\n",
" # save results in dict\n",
" city_suitable[ city ] = suited_roofs"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"city_suitable[ city ].head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Multiply everything with areas"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"city_suitable.keys()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for city, data in city_suitable.items():\n",
" data['total_std'] = np.sqrt( data['model_std']**2 + data['data_std']**2 )\n",
" \n",
" data['targ_kWh'] = data['target'] * data['FLAECHE']\n",
" data['pred_kWh'] = data['prediction'] * data['FLAECHE']\n",
" data['munc_kWh'] = data['model_std'] * data['FLAECHE']\n",
" data['dunc_kWh'] = data['data_std'] * data['FLAECHE']\n",
" data['tunc_kWh'] = data['total_std'] * data['FLAECHE']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Get city totals"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"results_GWh = city_suitable[ city ].sum() / 1e6"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"results_GWh"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Compute PV potential for communes and get inhabitants"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"eff = 0.17*0.8\n",
"cols = ['FLAECHE', 'available_area', 'targ_E_GWh', 'pred_E_GWh', 'munc_E_GWh', 'dunc_E_GWh', 'tunc_E_GWh']\n",
"results = pd.DataFrame( data = [], columns = cols )\n",
"\n",
"for city, data in city_suitable.items():\n",
" results_GWh = data.sum() / 1e6\n",
" \n",
" new_row = pd.DataFrame( data = [[results_GWh.FLAECHE, results_GWh.available_area, \n",
" results_GWh.targ_kWh * eff, results_GWh.pred_kWh * eff,\n",
" results_GWh.munc_kWh * eff, results_GWh.dunc_kWh * eff, \n",
" results_GWh.tunc_kWh * eff]], \n",
" columns = cols, index = [city] )\n",
" \n",
" results = results.append( new_row )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"results['dunc_rel_to_tunc'] = (results['tunc_E_GWh'] * results['dunc_E_GWh'] \n",
" / (results['dunc_E_GWh'] + results['munc_E_GWh'] ))\n",
"results['munc_rel_to_tunc'] = (results['tunc_E_GWh'] * results['munc_E_GWh'] \n",
" / (results['dunc_E_GWh'] + results['munc_E_GWh'] ))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# get number of inhabitants\n",
"results = results.merge(commune_data[['NAME', 'EINWOHNERZ']], \n",
" left_index = True, right_on = 'NAME', how = 'left').dropna().set_index('NAME')\n",
"results = results.sort_values('EINWOHNERZ', ascending = False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"results"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# BAR CHART"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# plot results\n",
"plt.figure()\n",
"\n",
"targ_idx = np.asarray(range(0, len(results))) * 2.5\n",
"pred_idx = targ_idx + 1\n",
"\n",
"plt.bar(targ_idx, results.targ_E_GWh , color = 'C1', width = 1.0, alpha = 0.8, label = 'Target')\n",
"plt.bar(pred_idx, results.pred_E_GWh , color = 'C0', width = 1.0, alpha = 0.5, label = 'Prediction')\n",
"\n",
"plt.errorbar( pred_idx, results.pred_E_GWh, yerr = results.tunc_E_GWh, \n",
" color = 'k', elinewidth = 2, capsize=2.5, \n",
" fmt = '.', markersize = 1, \n",
" label = 'Data uncertainty')\n",
"\n",
"plt.errorbar( pred_idx, results.pred_E_GWh, yerr = results.munc_rel_to_tunc, \n",
" color = 'r', elinewidth = 2, capsize=2.5, \n",
" fmt = '.', markersize = 1, \n",
" label = 'Model uncertainty')\n",
"\n",
"\n",
"plt.legend(loc = 'upper right')\n",
"plt.ylabel('PV potential per city (in $GWh)$')\n",
"\n",
"plt.xticks( targ_idx + 0.5, results.index )\n",
"\n",
"plt.tight_layout()\n",
"plt.savefig( os.path.join( 'pred_cities_allDataTrn.png'), dpi = 300)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### PV potential per inhabitants"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"results_per_inhab = results / results[['EINWOHNERZ']].values * 1e6"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# plot results\n",
"plt.figure()\n",
"\n",
"targ_idx = np.asarray(range(0, len(results_per_inhab))) * 2.5\n",
"pred_idx = targ_idx + 1\n",
"\n",
"plt.bar(targ_idx, results_per_inhab.targ_E_GWh , color = 'C1', width = 1.0, alpha = 0.8, label = 'Target')\n",
"plt.bar(pred_idx, results_per_inhab.pred_E_GWh , color = 'C0', width = 1.0, alpha = 0.5, label = 'Prediction')\n",
"\n",
"plt.errorbar( pred_idx, results_per_inhab.pred_E_GWh, yerr = results_per_inhab.tunc_E_GWh, \n",
" color = 'k', elinewidth = 2, capsize=2.5, \n",
" fmt = '.', markersize = 1, \n",
" label = 'Data uncertainty')\n",
"\n",
"plt.errorbar( pred_idx, results_per_inhab.pred_E_GWh, yerr = results_per_inhab.munc_rel_to_tunc, \n",
" color = 'r', elinewidth = 2, capsize=2.5, \n",
" fmt = '.', markersize = 1, \n",
" label = 'Model uncertainty')\n",
"\n",
"\n",
"plt.legend() # loc = 'upper right'\n",
"plt.ylabel('PV potential per inhabitant (in $kWh)$')\n",
"\n",
"plt.xticks( targ_idx + 0.5, results_per_inhab.index )\n",
"\n",
"plt.tight_layout()\n",
"plt.savefig( os.path.join( 'pred_cities_per_inhab_allDataTrn.png'), dpi = 300)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Get city information"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>NAME</th>\n",
" <th>EINWOHNERZ</th>\n",
" <th>GEM_FLAECH</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Davos</td>\n",
" <td>10937.0</td>\n",
" <td>28400.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77</th>\n",
" <td>Zürich</td>\n",
" <td>409241.0</td>\n",
" <td>9188.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>83</th>\n",
" <td>Lugano</td>\n",
" <td>63494.0</td>\n",
" <td>8621.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>296</th>\n",
" <td>Luzern</td>\n",
" <td>81401.0</td>\n",
" <td>3740.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>396</th>\n",
" <td>Basel</td>\n",
" <td>171513.0</td>\n",
" <td>2385.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1742</th>\n",
" <td>Interlaken</td>\n",
" <td>5592.0</td>\n",
" <td>427.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" NAME EINWOHNERZ GEM_FLAECH\n",
"6 Davos 10937.0 28400.0\n",
"77 Zürich 409241.0 9188.0\n",
"83 Lugano 63494.0 8621.0\n",
"296 Luzern 81401.0 3740.0\n",
"396 Basel 171513.0 2385.0\n",
"1742 Interlaken 5592.0 427.0"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"commune_data.loc[commune_data.NAME.isin(cities),['NAME', 'EINWOHNERZ', 'GEM_FLAECH']].dropna()"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Luzern\n",
"Number of buildings: 5940\n",
"Roof area: 3.25\n",
"Roof area per inhabitant: 39.95\n",
"\n",
"Zürich\n",
"Number of buildings: 46888\n",
"Roof area: 13.77\n",
"Roof area per inhabitant: 33.65\n",
"\n",
"Basel\n",
"Number of buildings: 11122\n",
"Roof area: 6.27\n",
"Roof area per inhabitant: 36.55\n",
"\n",
"Lugano\n",
"Number of buildings: 10052\n",
"Roof area: 3.10\n",
"Roof area per inhabitant: 48.85\n",
"\n",
"Interlaken\n",
"Number of buildings: 1025\n",
"Roof area: 0.48\n",
"Roof area per inhabitant: 85.06\n",
"\n",
"Davos\n",
"Number of buildings: 2582\n",
"Roof area: 1.21\n",
"Roof area per inhabitant: 110.49\n",
"\n"
]
}
],
"source": [
"# get remaining information\n",
"for city, data in city_data.items():\n",
" n_buildings = len(data.GWR_EGID.unique())\n",
" roof_area = data.FLAECHE.sum() / 1e6\n",
" \n",
" inhab = commune_data.loc[commune_data.NAME == city, 'EINWOHNERZ'].values\n",
" roof_area_per_inhab = roof_area / inhab * 1e6\n",
" \n",
" print(city)\n",
" print('Number of buildings: %d' %n_buildings)\n",
" print('Roof area: %.2f' %roof_area)\n",
" print('Roof area per inhabitant: %.2f\\n' %roof_area_per_inhab[0])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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